{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\aesmith2\Dropbox\Work\research in progress\Environmentalism and religion work\outputs\Kenya-Brazil\Writing and Presentations\Perspectives\Conditional Accept Submission\Honig Smith Bleck Replication Log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 8 Dec 2020, 19:20:54

{com}. do "C:\Users\aesmith2\Dropbox\Work\research in progress\Environmentalism and religion work\outputs\Kenya-Brazil\Quantitative analysis\Perspectives\Honig Smith Bleck Replication.do"
{txt}
{com}. 
. 
. ********************************************* REPLICATION CODE: ANALYSIS FOR "WHAT STYMIES ACTION ON CLIMATE CHANGE?" BY HONIG, SMITH, AND BLECK
. ********************************************* SUBMISSION FOR PERSPECTIVES ON POLITICS
. ********************************************* Analysis by Amy Erica Smith, amyericas@gmail.com
. 
. *** USE kenya afrobarometer R7, which may be downloaded from: http://afrobarometer.org/data
. use "ken_r7_data.dta", clear
{txt}
{com}. 
. 
. ****************************************************** INDEPENDENT VARIABLES ********************************************************************
. ***************************religion codings 
. **“None”: None, Agnostic, Atheist
. recode Q98 (0 28 29  . -1 = 1) (9998 9999 = .) (else = 0), g(None)
{txt}(1599 differences between Q98 and None)

{com}.         label var None "Q98: None, Agnostic, Atheist"
{txt}
{com}. **“Catholic”: Roman Catholic
. recode Q98 (2=1) (. -1 = .) (9998 9999 = .) (else = 0), g(Cath)
{txt}(1560 differences between Q98 and Cath)

{com}.         label var Cath "Q98 Catholic"
{txt}
{com}. **“Protestant/Evangelical”: ,Mennonite Anglican, Lutheran, Methodist, Presbyterian, Baptist, Quaker/Friends, Evangelical, Seventh Day Adventist, Dutch Reformed, Calvinist, Church of Christ,
. recode Q98 (5 6 7 8 9 10 11 12 16 31 32 30 = 1) (9998 9999 = .) (. -1 = .) (else = 0), g(Prot)
{txt}(1560 differences between Q98 and Prot)

{com}.         label var Prot "Q98: Anglican, Menn, Lutheran, Methodist, Presbyterian, Baptist, Quaker/Friends, Evangelical, SDA, Dutch Reform, Calvinist, Church of Christ"
{txt}note: label truncated to 80 characters

{com}. ***Anglican
. recode Q98 (5 = 1) (9998 9999 = .) (. -1 = .) (else = 0), g(Anglican)
{txt}(1560 differences between Q98 and Anglican)

{com}.         label var Anglican "Q98: Anglican; note also part of Prot"
{txt}
{com}. **“Pentecostal, AIC, and Other Christian”: Pentecostal, “Independent”, Zionist Christian Church, “Christian only”, Orthodox, Coptic, Jehovah's Witness, Mormon, 
. recode Q98 (1 3 4 13 15 17 33 14 9995 = 1) (9998 9999 = .) (. -1 = .) (else = 0), g(PentOther)
{txt}(1169 differences between Q98 and PentOther)

{com}.         // ** it turns out ALL the "other" mentions are Pentecostal/Independent churches. See:
.         tab1 Q98OTHER

{res}-> tabulation of Q98OTHER  

          {txt}Q98other. Religion of {c |}
          respondent (verbatim) {c |}      Freq.     Percent        Cum.
{hline 32}{c +}{hline 35}
                       4 SQUARE {c |}{res}          1        1.22        1.22
{txt}                4 SQUARE CHURCH {c |}{res}          1        1.22        2.44
{txt}                            ADC {c |}{res}          1        1.22        3.66
{txt}                      AFRICAN B {c |}{res}          1        1.22        4.88
{txt}      AFRICAN REVOLUTION KERUBI {c |}{res}          1        1.22        6.10
{txt}                            AIC {c |}{res}          6        7.32       13.41
{txt}                         AIPCEA {c |}{res}          1        1.22       14.63
{txt}AMBASSADORS OF CHRIST FELOWSHIP {c |}{res}          1        1.22       15.85
{txt}               APOSTOLIC CHURCH {c |}{res}          1        1.22       17.07
{txt}                            BAG {c |}{res}          1        1.22       18.29
{txt}           BIBLE BAPTIST CHURCH {c |}{res}          1        1.22       19.51
{txt}             CHRISTIAN COVENANT {c |}{res}          1        1.22       20.73
{txt}                  CHURCH OF GOD {c |}{res}          2        2.44       23.17
{txt}                       COVENANT {c |}{res}          2        2.44       25.61
{txt}             DELIVERANCE CHURCH {c |}{res}          2        2.44       28.05
{txt}                  DIVINE CHURCH {c |}{res}          1        1.22       29.27
{txt}                DOMINION CHURCH {c |}{res}          3        3.66       32.93
{txt}   EAST AFRICA PENTECOST CHURCH {c |}{res}          1        1.22       34.15
{txt}             FULL GOSPEL CHURCH {c |}{res}          3        3.66       37.80
{txt}      GLOBAL PRAISE AND WORSHIP {c |}{res}          1        1.22       39.02
{txt}       GRACE AND MIRACLE CHURCH {c |}{res}          2        2.44       41.46
{txt}                    HOLY SPIRIT {c |}{res}          1        1.22       42.68
{txt}                         ISRAEL {c |}{res}          1        1.22       43.90
{txt}                JESHI LA WOKOVU {c |}{res}          2        2.44       46.34
{txt}                     JESUS LIFE {c |}{res}          1        1.22       47.56
{txt}                            KAG {c |}{res}          2        2.44       50.00
{txt}          KENYA ASSEMBLY OF GOD {c |}{res}          1        1.22       51.22
{txt}                   KINGDOM LIFE {c |}{res}          1        1.22       52.44
{txt}                   LEGION MARIA {c |}{res}          1        1.22       53.66
{txt}                      LIFE CARE {c |}{res}          1        1.22       54.88
{txt}                MESSAGE BRIEFER {c |}{res}          1        1.22       56.10
{txt}                         MITUME {c |}{res}          1        1.22       57.32
{txt}                           NENO {c |}{res}          1        1.22       58.54
{txt}                  NEW APOSTOLIC {c |}{res}          1        1.22       59.76
{txt}     NEW LIFE CONVENTION CHURCH {c |}{res}          1        1.22       60.98
{txt}                          NOMIA {c |}{res}          1        1.22       62.20
{txt}                  NOMIYA SABATU {c |}{res}          1        1.22       63.41
{txt}                            PAG {c |}{res}          3        3.66       67.07
{txt}                           PEFA {c |}{res}          4        4.88       71.95
{txt}POWER OF JESUS AROUND THE WORLD {c |}{res}          1        1.22       73.17
{txt}                     PROTESTANT {c |}{res}          1        1.22       74.39
{txt}                REDEEMED GOSPEL {c |}{res}          1        1.22       75.61
{txt}         REDEEMERS FAITH CHURCH {c |}{res}          1        1.22       76.83
{txt}                       REFORMED {c |}{res}          2        2.44       79.27
{txt}             REVIVAL FELLOWSHIP {c |}{res}          1        1.22       80.49
{txt}                   RIVER JORDAN {c |}{res}          1        1.22       81.71
{txt}                           ROHO {c |}{res}          1        1.22       82.93
{txt}                    ROHO ISRAEL {c |}{res}          1        1.22       84.15
{txt}                 SALVATION ARMY {c |}{res}          7        8.54       92.68
{txt}               UNITED CHRISTIAN {c |}{res}          1        1.22       93.90
{txt}                        VICTORY {c |}{res}          2        2.44       96.34
{txt}             VOICE OF SALVATION {c |}{res}          1        1.22       97.56
{txt}                           VOSH {c |}{res}          2        2.44      100.00
{txt}{hline 32}{c +}{hline 35}
                          Total {c |}{res}         82      100.00
{txt}
{com}. label var Pent "Q98 Pentecostal, Independent, Other Christian Churches"
{txt}
{com}. **“Muslim”: Muslim only, Sunni only, Shia only
. recode Q98 (18 19 24 = 1) (9998 9999 = .) (. -1 = .) (else = 0), g(Musl)
{txt}(1560 differences between Q98 and Musl)

{com}.         label var Musl "Q98 Muslim, Sunni only, Shia only"
{txt}
{com}. **“Other”: Traditional/ethnic religion, Hindu, Bahai, Other
. recode Q98 (25 26 27 = 1) (9998 9999 = .) (. -1 = .) (else = 0), g(OTHERrel)
{txt}(1560 differences between Q98 and OTHERrel)

{com}.         label var OTHERrel "Q98:Traditional/ethnic religion, Hindu, Bahai, Other" 
{txt}
{com}.         *** it turns out there are only 3 of these people, once we code the people who said "other" as Pentecostals
.         
. ***** create a single variable for all religions
. egen religiousID = group(Musl PentOther Prot Cath None) if OTHERrel != 1
{txt}(19 missing values generated)

{com}.         lab def religiousID 1 "None" 2 "Catholic" 3 "Protestant" 4 "Pentecostal, Independent, & Other Christian" 5 "Muslim" 
{txt}
{com}.         lab val religiousID religiousID
{txt}
{com}.         lab var religiousID "Religious Identification"
{txt}
{com}. recode religiousID (1 = 1) (2 3 4 = 2) (5=3), g(religion)
{txt}(1171 differences between religiousID and religion)

{com}.         lab def religion 1 "None" 2 "Christian" 3 "Muslim"
{txt}
{com}.         lab val religion religion
{txt}
{com}.         
.         
. *************************** trust and group attitudes
. ***** trust in religious leaders
. g religioustrust = (Q43K/3) if Q43K < 8
{txt}(36 missing values generated)

{com}.         lab var religioustrust "Trust in Religious Leaders"
{txt}
{com}. recode religioustrust (0/.7 = 0) (1=1), g(highreltrust)
{txt}(644 differences between religioustrust and highreltrust)

{com}.  
. ***create state trust variable (trust: president, parliament, county govt, courts) Other vars not included ///
> ***(electoral commission, elected councillors, ruling party, opposition party, police, army)
. recode Q43A Q43B Q43E1_KEN Q43I (-1 8 9 = .)
{txt}(Q43A: 30 changes made)
(Q43B: 49 changes made)
(Q43E1_KEN: 49 changes made)
(Q43I: 86 changes made)

{com}. factor Q43A Q43B Q43E1_KEN Q43I
{txt}(obs=1,454)

Factor analysis/correlation{col 50}Number of obs    = {res}     1,454
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       2
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.25746      1.21936            1.3597       1.3597
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.03810      0.19081            0.0412       1.4009
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.15271      0.06532           -0.1651       1.2358
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.21803            .           -0.2358       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}  880.03{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Q43A}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5774}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1115}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6542}}}{space 1}
{space 4}{space 0}{ralign 12:Q43B}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6676}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0460}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5522}}}{space 1}
{space 4}{space 0}{ralign 12:Q43E1_KEN}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4133}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1368}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8104}}}{space 1}
{space 4}{space 0}{ralign 12:Q43I}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5545}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0695}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6876}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. egen statetrust = rowmean(Q43A Q43B Q43E1_KEN Q43I)
{txt}(14 missing values generated)

{com}.         replace statetrust = statetrust/3
{txt}(1,553 real changes made)

{com}.         label var statetrust "Trust in State"
{txt}
{com}.         recode statetrust (0/.7 = 0) (.71/1=1), g(highstatetrust)
{txt}(1446 differences between statetrust and highstatetrust)

{com}.  
. ***create gap in trust variable
. gen trustgap=religioustrust-statetrust 
{txt}(38 missing values generated)

{com}. label var trustgap "Trust Gap (Religious Leaders v. State)"
{txt}
{com}.  
. ***Ability to access information from the state 
. recode Q18* (-1 8 9  = .)
{txt}(Q18A: 90 changes made)
(Q18B: 168 changes made)
(Q18C: 173 changes made)
(Q18D: 211 changes made)

{com}. alpha Q18*

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .4447802
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.7120
{txt}
{com}. egen infoaccess = rowmean(Q18*)
{txt}(58 missing values generated)

{com}. replace infoaccess = infoaccess/3
{txt}(1,395 real changes made)

{com}.  
.  
. ***ethnicgrievance
. recode Q85A (-1 7 8 9 = 0), g(ethnicgrievance)
{txt}(93 differences between Q85A and ethnicgrievance)

{com}. replace ethnicgrievance = ethnicgrievance/3
{txt}variable {bf}ethnicgrievance{sf} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(843 real changes made)

{com}. lab var ethnicgrievance "Ethnic Grievances"
{txt}
{com}. 
. *** kenyan v ethnic ID
. recode Q85B (-1 7 8 9 = .) , g(kenyan_v_ethnicID)
{txt}(26 differences between Q85B and kenyan_v_ethnicID)

{com}. replace kenyan_v_ethnicID = (kenyan_v_ethnicID -1)/4
{txt}variable {bf}kenyan_v_ethnicID{sf} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(1,573 real changes made)

{com}. lab var kenyan_v_ethnicID "Kenyan v. Ethnic ID"
{txt}
{com}. 
. *** discrimination
. recode Q86AD (-1 8 9 = 0), g(ethnicdiscrimination)
{txt}(23 differences between Q86AD and ethnicdiscrimination)

{com}. replace ethnicdiscrimination = ethnicdiscrimination/3
{txt}variable {bf}ethnicdiscrimination{sf} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(359 real changes made)

{com}. 
. egen grievance2 = rowmean(ethnicgrievance ethnicdiscrimination) 
{txt}
{com}.  
. *************************** demographics
. ***Male
. gen Male=Q101
{txt}
{com}. recode Male 2=0
{txt}(Male: 799 changes made)

{com}. label var Male "Male"
{txt}
{com}. 
.  **rural/urban
.  gen Urban=urbrur
{txt}
{com}.  recode Urban 2=0
{txt}(Urban: 1022 changes made)

{com}.  
. **education
. gen Secondary=0
{txt}
{com}. replace Secondary=1 if Q97==5
{txt}(372 real changes made)

{com}. replace Secondary=. if Q97==-1
{txt}(15 real changes made, 15 to missing)

{com}. replace Secondary=. if Q97==98
{txt}(3 real changes made, 3 to missing)

{com}. replace Secondary=. if Q97==99
{txt}(0 real changes made)

{com}. replace Secondary=1 if Q97>5
{txt}(296 real changes made)

{com}. label var Secondary "Q97: Completed secondary and beyond"
{txt}
{com}. 
. gen Education =Q97
{txt}
{com}.         replace Education=. if Q97==-1
{txt}(15 real changes made, 15 to missing)

{com}.         replace Education=. if Q97==98
{txt}(3 real changes made, 3 to missing)

{com}.         replace Education=. if Q97==99
{txt}(0 real changes made)

{com}.         label var Education "Education"
{txt}
{com}.  
. gen NoSchool=Education
{txt}(18 missing values generated)

{com}. replace NoSchool=77 if Education==0
{txt}(100 real changes made)

{com}. replace NoSchool=0 if Education>0
{txt}(1,499 real changes made)

{com}. recode NoSchool 77=1
{txt}(NoSchool: 100 changes made)

{com}.  
. label var NoSchool "Q97: No formal schooling"
{txt}
{com}.  
. **youth
. gen Youth=Q1
{txt}
{com}. replace Youth=0 if Q1>35
{txt}(672 real changes made)

{com}. replace Youth=. if Q1==999
{txt}(7 real changes made, 7 to missing)

{com}. replace Youth=1 if Q1>17 & Q1<36
{txt}(927 real changes made)

{com}. label var Youth "Q1: HH 35 or under"
{txt}
{com}. 
. 
. gen Age=Q1
{txt}
{com}. replace Age=. if Q1==998
{txt}(0 real changes made)

{com}. replace Age=. if Q1==999
{txt}(7 real changes made, 7 to missing)

{com}. replace Age=. if Q1==-1
{txt}(0 real changes made)

{com}. label var Age "Age"
{txt}
{com}. 
. 
. **no food
. gen Hunger=Q8A
{txt}
{com}. replace Hunger =. if Q8A==-1
{txt}(0 real changes made)

{com}. replace Hunger =. if Q8A==8
{txt}(0 real changes made)

{com}. replace Hunger =. if Q8A==9
{txt}(1 real change made, 1 to missing)

{com}. label var Hunger "Food shortage frequency"
{txt}
{com}. 
. **ethnicity
. recode Q84 (-1 9995 9998 = .)
{txt}(Q84: 102 changes made)

{com}. recode Q84 (307=1) (else = 0), g(masai)
{txt}(1599 differences between Q84 and masai)

{com}.         lab var masai "Masai/Samburu"
{txt}
{com}. recode Q84 (310=1) (else = 0), g(somali)
{txt}(1599 differences between Q84 and somali)

{com}.         lab var somali "Somali"
{txt}
{com}. recode Q84 (307 310 312 311 = 1) (else = 0), g(pastoralists)
{txt}(1599 differences between Q84 and pastoralists)

{com}.         lab var pastoralists "Pastoralist"
{txt}
{com}.         recode pastoralists (1=0) if Urban == 1
{txt}(pastoralists: 33 changes made)

{com}. 
. ** difficulty answering questions       
. g difficulty = Q108/4
{txt}
{com}.         lab var difficulty "Difficulty Answering Questions"
{txt}
{com}. 
. ********************************************** CLIMATE CHANGE ATTITUDES *****************************************************************
. 
. ************** salience of climate change: Climate worsening?
. recode Q72 (-1=.) (1 2 = 2) (3 = 1) (4 5 = 0) (9=.), g(climateperception)
{txt}(1112 differences between Q72 and climateperception)

{com}.         lab def climateperception 2 "Worse" 1 "Same" 0 "Better"
{txt}
{com}.         lab val climateperception climateperception
{txt}
{com}. recode climateperception (2=1) (1 0 = 0), g(climateworsening)
{txt}(1165 differences between climateperception and climateworsening)

{com}.         lab var climateworsening "Climate Worsening"
{txt}
{com}. 
. ************** salience of climate change: "Most Important Problem"
. *Farming/Agriculture: Option 7
. *Food Shortage/Famine: 8
. *Drought: 9
. *Water supply: 17
. g mip_1st = Q55PT1 == 7 | Q55PT1 == 8 | Q55PT1 == 9 | Q55PT1 == 17 
{txt}
{com}.         g mip_2nd = Q55PT2 == 7 | Q55PT2 == 8 | Q55PT2 == 9 | Q55PT2 == 17 
{txt}
{com}.         g mip_3rd = Q55PT3 == 7 | Q55PT3 == 8 | Q55PT3 == 9 | Q55PT3 == 17 
{txt}
{com}. g mip_any = mip_1st == 1 | mip_2nd == 1 | mip_3rd == 1 
{txt}
{com}. 
. g mipr_1st = Q55PT1 == 8 | Q55PT1 == 9 | Q55PT1 == 17 
{txt}
{com}.         g mipr_2nd = Q55PT2 == 8 | Q55PT2 == 9 | Q55PT2 == 17 
{txt}
{com}.         g mipr_3rd = Q55PT3 == 8 | Q55PT3 == 9 | Q55PT3 == 17 
{txt}
{com}. g mipr_any = mipr_1st == 1 | mipr_2nd == 1 | mipr_3rd == 1 
{txt}
{com}. 
. 
. ************** Efficacy: Can ordinary people do something?
. recode Q78 (1=0) (2 3 = 1) (-1 0 7 8 9 . = .), g(CollectiveA)
{txt}(1599 differences between Q78 and CollectiveA)

{com}.         label var CollectiveA "Binary of Q78: Ordinary Kenyans can do something (little&alot)"
{txt}
{com}.         label define CollectiveALL 0 "Ordinary Kenyans can do nothing at all" 1 "Ordinary Kenyans can do a little or a lot" 
{txt}
{com}.         label values CollectiveA CollectiveALL
{txt}
{com}. 
. recode Q78 (1=0) (2=1) (3=2) (-1 0 7 8 9 . = .), g(CollectiveA_2)
{txt}(1599 differences between Q78 and CollectiveA_2)

{com}.         label var CollectiveA_2 "Q78: Ordinary Kenyans can do nothing, a little, a lot"
{txt}
{com}.         label define CollectiveAl 0 "Ordinary Kenyans can do nothing at all" 1 "Ordinary Kenyans can do a little bit" 2 " Ordinary Kenyans can do a lot"
{txt}
{com}.         label values CollectiveA_2 CollectiveAl
{txt}
{com}. 
. recode Q78 (1=1) (2 3 = 0) (-1 0 7 8 9 . = .), g(kenyanscandonothing)
{txt}(1456 differences between Q78 and kenyanscandonothing)

{com}. recode Q78 (1 2 =0) (3 = 1) (-1 0 7 8 9 . = .), g(kenyanscandoalot)
{txt}(1599 differences between Q78 and kenyanscandoalot)

{com}.         
.         
. ************** BELIEF IN ANTHROPOGENIC CLIMATE CHANGE
. **x Q75. Climate change: main cause 
. gen MainCause=Q75
{txt}
{com}.         replace MainCause=. if Q75== -1 | Q75== 7 | Q75== 8 | Q75== 9 | Q75== .
{txt}(611 real changes made, 611 to missing)

{com}.         label define MainCauseL 1 "Human Activity" 2 "Natural Processes" 3 "Both Human Activity and Natural Processes" 4 "None of these"
{txt}
{com}.         label values MainCause MainCauseL
{txt}
{com}. gen HumanA=0
{txt}
{com}.         replace HumanA=1 if Q75==1
{txt}(616 real changes made)

{com}.         replace HumanA=. if Q75== -1 | Q75== 7 | Q75== 8 | Q75== 9 | Q75== .
{txt}(611 real changes made, 611 to missing)

{com}.         label var HumanA "Believes in only human activity as cause"
{txt}
{com}. gen BothHumanA=HumanA
{txt}(611 missing values generated)

{com}.         replace BothHumanA =1 if MainCause==3
{txt}(130 real changes made)

{com}.         label var BothHumanA "Q75: dummy for any belief in human agency"
{txt}
{com}. 
. 
. ****************************** Selection variables: who got the efficacy question?
. recode Q73 (-1 9 = 0), g(HeardCC)
{txt}(49 differences between Q73 and HeardCC)

{com}.         label var HeardCC "Has heard of climate change"
{txt}
{com}. 
. recode Q78 (1 2 3=1) (0 -1 9 . = 0) (7 = .), g(StopCC)
{txt}(1230 differences between Q78 and StopCC)

{com}.         label define StopCC 0 "Heard of CC, but doesn't need to be stopped" 1 "Heard of CC, CC does need to be stopped"
{txt}
{com}.         recode StopCC 0 = . if HeardCC == 0
{txt}(StopCC: 48 changes made)

{com}. 
. recode CollectiveA_2 (0 1 2 = 1) (. = 0), g(got_efficacy_q)
{txt}(1334 differences between CollectiveA_2 and got_efficacy_q)

{com}.         
. 
. 
. **************************************************************************************************************************************************************************************
. ********************************************************************************** RESULTS *******************************************************************************************
. ************ In text discussion of efficacy ************************************************************************
. mean kenyanscandonothing [pweight= withinwt]
{res}
{txt}Mean estimation{col 42}Number of obs{col 58}= {res}       751

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 21}{c |}       Mean{col 33}   Std. Err.{col 45}     [95% Con{col 58}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
kenyanscandonothing {c |}{col 21}{res}{space 2} .1867973{col 33}{space 2} .0153581{col 44}{space 5} .1566474{col 58}{space 3} .2169472
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. mean kenyanscandoalot [pweight= withinwt]
{res}
{txt}Mean estimation{col 39}Number of obs{col 55}= {res}       751

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 18}{c |}       Mean{col 30}   Std. Err.{col 42}     [95% Con{col 55}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
kenyanscandoalot {c |}{col 18}{res}{space 2} .4520191{col 30}{space 2} .0197585{col 41}{space 5} .4132306{col 55}{space 3} .4908077
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. 
. ************ Figure 1: Efficacy (from multivariate analysis) ***************************************************************************
. global indvars Education Age Urban Hunger Male masai somali pastoralists i.region 
{txt}
{com}. global modelspecs [pweight= withinwt], cluster(region)
{txt}
{com}. preserve
{txt}
{com}.         ologit CollectiveA_2 b5.religion  $indvars $modelspecs

{txt}note: 3.religion omitted because of collinearity
note: 5.religion identifies no observations in the sample
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-763.18606}  
Iteration 1:{space 3}log pseudolikelihood = {res:-722.55511}  
Iteration 2:{space 3}log pseudolikelihood = {res:-722.05769}  
Iteration 3:{space 3}log pseudolikelihood = {res:-722.05703}  
Iteration 4:{space 3}log pseudolikelihood = {res:-722.05703}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       743
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-722.05703{txt}{col 49}Pseudo R2{col 67}= {res}    0.0539

{txt}{ralign 80:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1} CollectiveA_2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}religion {c |}
{space 9}None  {c |}{col 16}{res}{space 2} .9697472{col 28}{space 2}  .605431{col 39}{space 1}    1.60{col 48}{space 3}0.109{col 56}{space 4}-.2168759{col 69}{space 3}  2.15637
{txt}{space 4}Christian  {c |}{col 16}{res}{space 2} .9152423{col 28}{space 2} .5024224{col 39}{space 1}    1.82{col 48}{space 3}0.069{col 56}{space 4}-.0694875{col 69}{space 3} 1.899972
{txt}{space 7}Muslim  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 12}5  {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (empty)
{space 14} {c |}
{space 5}Education {c |}{col 16}{res}{space 2} .1264595{col 28}{space 2} .0769267{col 39}{space 1}    1.64{col 48}{space 3}0.100{col 56}{space 4} -.024314{col 69}{space 3} .2772331
{txt}{space 11}Age {c |}{col 16}{res}{space 2} .0022379{col 28}{space 2} .0063457{col 39}{space 1}    0.35{col 48}{space 3}0.724{col 56}{space 4}-.0101996{col 69}{space 3} .0146753
{txt}{space 9}Urban {c |}{col 16}{res}{space 2} .0922088{col 28}{space 2} .1353296{col 39}{space 1}    0.68{col 48}{space 3}0.496{col 56}{space 4}-.1730324{col 69}{space 3}   .35745
{txt}{space 8}Hunger {c |}{col 16}{res}{space 2}-.1393538{col 28}{space 2}  .097137{col 39}{space 1}   -1.43{col 48}{space 3}0.151{col 56}{space 4}-.3297389{col 69}{space 3} .0510313
{txt}{space 10}Male {c |}{col 16}{res}{space 2}  .019577{col 28}{space 2} .1675487{col 39}{space 1}    0.12{col 48}{space 3}0.907{col 56}{space 4}-.3088124{col 69}{space 3} .3479663
{txt}{space 9}masai {c |}{col 16}{res}{space 2}-.4459227{col 28}{space 2} .3045225{col 39}{space 1}   -1.46{col 48}{space 3}0.143{col 56}{space 4}-1.042776{col 69}{space 3} .1509303
{txt}{space 8}somali {c |}{col 16}{res}{space 2} .3239033{col 28}{space 2} .6346436{col 39}{space 1}    0.51{col 48}{space 3}0.610{col 56}{space 4}-.9199753{col 69}{space 3} 1.567782
{txt}{space 2}pastoralists {c |}{col 16}{res}{space 2}-.6318863{col 28}{space 2} .2080188{col 39}{space 1}   -3.04{col 48}{space 3}0.002{col 56}{space 4}-1.039596{col 69}{space 3} -.224177
{txt}{space 14} {c |}
{space 8}region {c |}
{space 6}Central  {c |}{col 16}{res}{space 2} .4645285{col 28}{space 2} .0768868{col 39}{space 1}    6.04{col 48}{space 3}0.000{col 56}{space 4} .3138332{col 69}{space 3} .6152237
{txt}{space 6}Eastern  {c |}{col 16}{res}{space 2} .3887105{col 28}{space 2} .1152328{col 39}{space 1}    3.37{col 48}{space 3}0.001{col 56}{space 4} .1628583{col 69}{space 3} .6145627
{txt}{space 2}Rift Valley  {c |}{col 16}{res}{space 2} .4934711{col 28}{space 2} .0860536{col 39}{space 1}    5.73{col 48}{space 3}0.000{col 56}{space 4} .3248092{col 69}{space 3} .6621331
{txt}{space 7}Nyanza  {c |}{col 16}{res}{space 2}   .63149{col 28}{space 2}  .123997{col 39}{space 1}    5.09{col 48}{space 3}0.000{col 56}{space 4} .3884604{col 69}{space 3} .8745197
{txt}{space 6}Western  {c |}{col 16}{res}{space 2}-.4212897{col 28}{space 2} .1477708{col 39}{space 1}   -2.85{col 48}{space 3}0.004{col 56}{space 4}-.7109151{col 69}{space 3}-.1316643
{txt}North Eastern  {c |}{col 16}{res}{space 2}-.4287678{col 28}{space 2} .2474767{col 39}{space 1}   -1.73{col 48}{space 3}0.083{col 56}{space 4}-.9138133{col 69}{space 3} .0562778
{txt}{space 8}Coast  {c |}{col 16}{res}{space 2}-.3798716{col 28}{space 2} .2087524{col 39}{space 1}   -1.82{col 48}{space 3}0.069{col 56}{space 4}-.7890188{col 69}{space 3} .0292756
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2} .0767425{col 28}{space 2} .8467927{col 56}{space 4}-1.582941{col 69}{space 3} 1.736426
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2} 1.910678{col 28}{space 2} .7472956{col 56}{space 4} .4460055{col 69}{space 3}  3.37535
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.                 margins religion, post level(76)
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       743
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._predict}:{space 1}{res:Pr(CollectiveA_2==0), predict(pr outcome(0))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._predict}:{space 1}{res:Pr(CollectiveA_2==1), predict(pr outcome(1))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._predict}:{space 1}{res:Pr(CollectiveA_2==2), predict(pr outcome(2))}{p_end}
{p2colreset}{...}

{res}{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31} Delta-method
{col 19}{c |}     Margin{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [76% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_predict#religion {c |}
{space 10}1#None  {c |}{col 19}{res}{space 2} .1646309{col 31}{space 2}  .100285{col 42}{space 1}    1.64{col 51}{space 3}0.101{col 59}{space 4} .0467974{col 72}{space 3} .2824645
{txt}{space 5}1#Christian  {c |}{col 19}{res}{space 2} .1719003{col 31}{space 2} .0137798{col 42}{space 1}   12.47{col 51}{space 3}0.000{col 59}{space 4} .1557092{col 72}{space 3} .1880914
{txt}{space 8}1#Muslim  {c |}{col 19}{res}{space 2} .3300217{col 31}{space 2} .1117941{col 42}{space 1}    2.95{col 51}{space 3}0.003{col 59}{space 4} .1986651{col 72}{space 3} .4613783
{txt}{space 10}2#None  {c |}{col 19}{res}{space 2} .3609184{col 31}{space 2} .0708591{col 42}{space 1}    5.09{col 51}{space 3}0.000{col 59}{space 4} .2776599{col 72}{space 3}  .444177
{txt}{space 5}2#Christian  {c |}{col 19}{res}{space 2} .3664152{col 31}{space 2} .0292759{col 42}{space 1}   12.52{col 51}{space 3}0.000{col 59}{space 4} .3320164{col 72}{space 3} .4008139
{txt}{space 8}2#Muslim  {c |}{col 19}{res}{space 2}  .405426{col 31}{space 2} .0397715{col 42}{space 1}   10.19{col 51}{space 3}0.000{col 59}{space 4} .3586949{col 72}{space 3}  .452157
{txt}{space 10}3#None  {c |}{col 19}{res}{space 2} .4744507{col 31}{space 2}  .165879{col 42}{space 1}    2.86{col 51}{space 3}0.004{col 59}{space 4}  .279545{col 72}{space 3} .6693563
{txt}{space 5}3#Christian  {c |}{col 19}{res}{space 2} .4616845{col 31}{space 2} .0187843{col 42}{space 1}   24.58{col 51}{space 3}0.000{col 59}{space 4} .4396132{col 72}{space 3} .4837558
{txt}{space 8}3#Muslim  {c |}{col 19}{res}{space 2} .2645524{col 31}{space 2} .0833152{col 42}{space 1}    3.18{col 51}{space 3}0.001{col 59}{space 4} .1666581{col 72}{space 3} .3624466
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.                 parmest, label norestore level(76)
{res}{txt}
{com}. egen dv = seq(), f(1) t(3) block(3)
{txt}
{com}.         drop if dv == 2
{txt}(3 observations deleted)

{com}.         recode dv (3=2)
{txt}(dv: 3 changes made)

{com}.         lab def dv 1 "Kenyans Can Do Nothing" 2 "Kenyans Can Do a Lot", modify
{txt}
{com}.         lab val dv dv
{txt}
{com}. egen religion = seq(), f(1) t(3)
{txt}
{com}.         lab def religion 1 "None" 2 "Christian" 3 "Muslim", modify
{txt}
{com}.         lab val religion religion
{txt}
{com}. g xaxis = _n + (dv-1)
{txt}
{com}. graph twoway (bar estimate xaxis if religion == 1, lcolor(black) fcolor(gs5) barwidth(0.8)) ///
>                         (bar estimate xaxis if religion == 2, lcolor(black) fcolor(gs10) barwidth(0.8)) ///
>                         (bar estimate xaxis if religion == 3, lcolor(black) fcolor(gs15) barwidth(0.8)) ///
>                         (rspike min76 max76 xaxis, lcolor(gs10)) , ///
>         graphregion(lcolor(black) fcolor(white)) ytitle("Predicted Probability of Saying Kenyans Can Do:", margin(small)) yscale(r(0)) ///
>         xlabel(2 "Nothing" 6 "A Lot", notick) xtitle("") legend(order(1 "None" 2 "Christian" 3 "Muslim") col(3) symxsize(10) span)
{res}{txt}
{com}. restore
{txt}
{com}. 
. 
. ************ In text discussion of issue salience ************************************************************************
. mean climateworsening [pweight= withinwt]
{res}
{txt}Mean estimation{col 39}Number of obs{col 55}= {res}     1,462

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 18}{c |}       Mean{col 30}   Std. Err.{col 42}     [95% Con{col 55}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
climateworsening {c |}{col 18}{res}{space 2} .5579256{col 30}{space 2} .0140886{col 41}{space 5} .5302894{col 55}{space 3} .5855617
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. mean mip_any [pweight= withinwt]
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}     1,599

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 5}mip_any {c |}{col 14}{res}{space 2} .3838377{col 26}{space 2} .0132012{col 37}{space 5} .3579442{col 51}{space 3} .4097312
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. mean mip_1st [pweight= withinwt]
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}     1,599

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 5}mip_1st {c |}{col 14}{res}{space 2} .1499274{col 26}{space 2} .0096856{col 37}{space 5} .1309297{col 51}{space 3} .1689252
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. 
. *********** in-text discussion of predicted probs when issue salience is added to efficacy model ************************************************************************
. ologit CollectiveA_2 3.religion ethnicgrievance kenyan_v_ethnicID climateworsening $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-748.78198}  
Iteration 1:{space 3}log pseudolikelihood = {res:-707.21773}  
Iteration 2:{space 3}log pseudolikelihood = {res:-706.71495}  
Iteration 3:{space 3}log pseudolikelihood = {res:-706.71427}  
Iteration 4:{space 3}log pseudolikelihood = {res:-706.71427}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       729
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-706.71427{txt}{col 49}Pseudo R2{col 67}= {res}    0.0562

{txt}{ralign 83:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}    CollectiveA_2{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}religion {c |}
{space 10}Muslim  {c |}{col 19}{res}{space 2}-1.158056{col 31}{space 2} .3567045{col 42}{space 1}   -3.25{col 51}{space 3}0.001{col 59}{space 4}-1.857184{col 72}{space 3}-.4589278
{txt}{space 2}ethnicgrievance {c |}{col 19}{res}{space 2}-.0279033{col 31}{space 2} .2837833{col 42}{space 1}   -0.10{col 51}{space 3}0.922{col 59}{space 4}-.5841084{col 72}{space 3} .5283018
{txt}kenyan_v_ethnicID {c |}{col 19}{res}{space 2}-.3485799{col 31}{space 2} .4407043{col 42}{space 1}   -0.79{col 51}{space 3}0.429{col 59}{space 4}-1.212344{col 72}{space 3} .5151846
{txt}{space 1}climateworsening {c |}{col 19}{res}{space 2}-.1140898{col 31}{space 2} .0610157{col 42}{space 1}   -1.87{col 51}{space 3}0.062{col 59}{space 4}-.2336784{col 72}{space 3} .0054989
{txt}{space 8}Education {c |}{col 19}{res}{space 2} .1129187{col 31}{space 2} .0702387{col 42}{space 1}    1.61{col 51}{space 3}0.108{col 59}{space 4}-.0247467{col 72}{space 3} .2505841
{txt}{space 14}Age {c |}{col 19}{res}{space 2} .0014562{col 31}{space 2} .0051728{col 42}{space 1}    0.28{col 51}{space 3}0.778{col 59}{space 4}-.0086822{col 72}{space 3} .0115947
{txt}{space 12}Urban {c |}{col 19}{res}{space 2} .1255431{col 31}{space 2} .1314188{col 42}{space 1}    0.96{col 51}{space 3}0.339{col 59}{space 4} -.132033{col 72}{space 3} .3831192
{txt}{space 11}Hunger {c |}{col 19}{res}{space 2}-.1335379{col 31}{space 2} .1015578{col 42}{space 1}   -1.31{col 51}{space 3}0.189{col 59}{space 4}-.3325875{col 72}{space 3} .0655117
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0444488{col 31}{space 2}  .152142{col 42}{space 1}    0.29{col 51}{space 3}0.770{col 59}{space 4}-.2537441{col 72}{space 3} .3426416
{txt}{space 12}masai {c |}{col 19}{res}{space 2}-.4565295{col 31}{space 2} .3101385{col 42}{space 1}   -1.47{col 51}{space 3}0.141{col 59}{space 4} -1.06439{col 72}{space 3} .1513307
{txt}{space 11}somali {c |}{col 19}{res}{space 2} .6743163{col 31}{space 2} .5154413{col 42}{space 1}    1.31{col 51}{space 3}0.191{col 59}{space 4}-.3359302{col 72}{space 3} 1.684563
{txt}{space 5}pastoralists {c |}{col 19}{res}{space 2}-.5733438{col 31}{space 2} .2424248{col 42}{space 1}   -2.37{col 51}{space 3}0.018{col 59}{space 4}-1.048488{col 72}{space 3}-.0981999
{txt}{space 17} {c |}
{space 11}region {c |}
{space 9}Central  {c |}{col 19}{res}{space 2} .4426154{col 31}{space 2} .0590019{col 42}{space 1}    7.50{col 51}{space 3}0.000{col 59}{space 4} .3269738{col 72}{space 3}  .558257
{txt}{space 9}Eastern  {c |}{col 19}{res}{space 2} .4032884{col 31}{space 2} .0925335{col 42}{space 1}    4.36{col 51}{space 3}0.000{col 59}{space 4}  .221926{col 72}{space 3} .5846508
{txt}{space 5}Rift Valley  {c |}{col 19}{res}{space 2} .4681658{col 31}{space 2}  .085334{col 42}{space 1}    5.49{col 51}{space 3}0.000{col 59}{space 4} .3009143{col 72}{space 3} .6354174
{txt}{space 10}Nyanza  {c |}{col 19}{res}{space 2} .6951712{col 31}{space 2} .1007374{col 42}{space 1}    6.90{col 51}{space 3}0.000{col 59}{space 4} .4977295{col 72}{space 3} .8926128
{txt}{space 9}Western  {c |}{col 19}{res}{space 2}-.3968354{col 31}{space 2} .1368778{col 42}{space 1}   -2.90{col 51}{space 3}0.004{col 59}{space 4}-.6651111{col 72}{space 3}-.1285598
{txt}{space 3}North Eastern  {c |}{col 19}{res}{space 2}-.4868547{col 31}{space 2} .3022414{col 42}{space 1}   -1.61{col 51}{space 3}0.107{col 59}{space 4}-1.079237{col 72}{space 3} .1055276
{txt}{space 11}Coast  {c |}{col 19}{res}{space 2}-.2672673{col 31}{space 2} .1913743{col 42}{space 1}   -1.40{col 51}{space 3}0.163{col 59}{space 4}-.6423541{col 72}{space 3} .1078195
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-1.201111{col 31}{space 2} .6045039{col 59}{space 4}-2.385917{col 72}{space 3}-.0163056
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2} .6096921{col 31}{space 2} .5093624{col 59}{space 4}-.3886399{col 72}{space 3} 1.608024
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         margins religion
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       729
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._predict}:{space 1}{res:Pr(CollectiveA_2==0), predict(pr outcome(0))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._predict}:{space 1}{res:Pr(CollectiveA_2==1), predict(pr outcome(1))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._predict}:{space 1}{res:Pr(CollectiveA_2==2), predict(pr outcome(2))}{p_end}
{p2colreset}{...}

{res}{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31} Delta-method
{col 19}{c |}     Margin{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_predict#religion {c |}
{space 10}1#None  {c |}{col 19}{res}{space 2} .1711991{col 31}{space 2} .0143072{col 42}{space 1}   11.97{col 51}{space 3}0.000{col 59}{space 4} .1431576{col 72}{space 3} .1992407
{txt}{space 5}1#Christian  {c |}{col 19}{res}{space 2} .1711991{col 31}{space 2} .0143072{col 42}{space 1}   11.97{col 51}{space 3}0.000{col 59}{space 4} .1431576{col 72}{space 3} .1992407
{txt}{space 8}1#Muslim  {c |}{col 19}{res}{space 2} .3820399{col 31}{space 2} .0892157{col 42}{space 1}    4.28{col 51}{space 3}0.000{col 59}{space 4} .2071803{col 72}{space 3} .5568995
{txt}{space 10}2#None  {c |}{col 19}{res}{space 2} .3620559{col 31}{space 2} .0274741{col 42}{space 1}   13.18{col 51}{space 3}0.000{col 59}{space 4} .3082076{col 72}{space 3} .4159042
{txt}{space 5}2#Christian  {c |}{col 19}{res}{space 2} .3620559{col 31}{space 2} .0274741{col 42}{space 1}   13.18{col 51}{space 3}0.000{col 59}{space 4} .3082076{col 72}{space 3} .4159042
{txt}{space 8}2#Muslim  {c |}{col 19}{res}{space 2}  .392891{col 31}{space 2} .0451363{col 42}{space 1}    8.70{col 51}{space 3}0.000{col 59}{space 4} .3044255{col 72}{space 3} .4813566
{txt}{space 10}3#None  {c |}{col 19}{res}{space 2}  .466745{col 31}{space 2} .0144449{col 42}{space 1}   32.31{col 51}{space 3}0.000{col 59}{space 4} .4384336{col 72}{space 3} .4950564
{txt}{space 5}3#Christian  {c |}{col 19}{res}{space 2}  .466745{col 31}{space 2} .0144449{col 42}{space 1}   32.31{col 51}{space 3}0.000{col 59}{space 4} .4384336{col 72}{space 3} .4950564
{txt}{space 8}3#Muslim  {c |}{col 19}{res}{space 2} .2250691{col 31}{space 2}  .053237{col 42}{space 1}    4.23{col 51}{space 3}0.000{col 59}{space 4} .1207264{col 72}{space 3} .3294118
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         
.         
. ******************************* Table 1: Determinants of Efficacy ************************************************************************
. global indvars masai somali pastoralists i.region Education Age Urban Hunger Male 
{txt}
{com}. global modelspecs [pweight= withinwt], cluster(region)
{txt}
{com}. eststo clear
{txt}
{com}. ologit CollectiveA_2 3.religion $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-763.18606}  
Iteration 1:{space 3}log pseudolikelihood = {res:-722.55735}  
Iteration 2:{space 3}log pseudolikelihood = {res:-722.06276}  
Iteration 3:{space 3}log pseudolikelihood = {res:-722.06209}  
Iteration 4:{space 3}log pseudolikelihood = {res:-722.06209}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       743
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-722.06209{txt}{col 49}Pseudo R2{col 67}= {res}    0.0539

{txt}{ralign 80:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1} CollectiveA_2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}religion {c |}
{space 7}Muslim  {c |}{col 16}{res}{space 2}-.9173269{col 28}{space 2} .4854777{col 39}{space 1}   -1.89{col 48}{space 3}0.059{col 56}{space 4}-1.868846{col 69}{space 3} .0341919
{txt}{space 9}masai {c |}{col 16}{res}{space 2}-.4452416{col 28}{space 2} .3047482{col 39}{space 1}   -1.46{col 48}{space 3}0.144{col 56}{space 4}-1.042537{col 69}{space 3} .1520538
{txt}{space 8}somali {c |}{col 16}{res}{space 2} .3242697{col 28}{space 2} .6312975{col 39}{space 1}    0.51{col 48}{space 3}0.607{col 56}{space 4}-.9130505{col 69}{space 3}  1.56159
{txt}{space 2}pastoralists {c |}{col 16}{res}{space 2}-.6311088{col 28}{space 2} .2092991{col 39}{space 1}   -3.02{col 48}{space 3}0.003{col 56}{space 4}-1.041327{col 69}{space 3}-.2208902
{txt}{space 14} {c |}
{space 8}region {c |}
{space 6}Central  {c |}{col 16}{res}{space 2} .4626945{col 28}{space 2} .0620698{col 39}{space 1}    7.45{col 48}{space 3}0.000{col 56}{space 4} .3410399{col 69}{space 3} .5843491
{txt}{space 6}Eastern  {c |}{col 16}{res}{space 2} .3868536{col 28}{space 2} .0981702{col 39}{space 1}    3.94{col 48}{space 3}0.000{col 56}{space 4} .1944436{col 69}{space 3} .5792636
{txt}{space 2}Rift Valley  {c |}{col 16}{res}{space 2} .4939468{col 28}{space 2} .0896741{col 39}{space 1}    5.51{col 48}{space 3}0.000{col 56}{space 4} .3181888{col 69}{space 3} .6697047
{txt}{space 7}Nyanza  {c |}{col 16}{res}{space 2} .6293665{col 28}{space 2} .1030602{col 39}{space 1}    6.11{col 48}{space 3}0.000{col 56}{space 4} .4273722{col 69}{space 3} .8313608
{txt}{space 6}Western  {c |}{col 16}{res}{space 2}-.4228256{col 28}{space 2} .1385763{col 39}{space 1}   -3.05{col 48}{space 3}0.002{col 56}{space 4}-.6944301{col 69}{space 3}-.1512211
{txt}North Eastern  {c |}{col 16}{res}{space 2}-.4300067{col 28}{space 2} .2352722{col 39}{space 1}   -1.83{col 48}{space 3}0.068{col 56}{space 4}-.8911318{col 69}{space 3} .0311184
{txt}{space 8}Coast  {c |}{col 16}{res}{space 2}-.3800326{col 28}{space 2} .2093957{col 39}{space 1}   -1.81{col 48}{space 3}0.070{col 56}{space 4}-.7904407{col 69}{space 3} .0303755
{txt}{space 14} {c |}
{space 5}Education {c |}{col 16}{res}{space 2} .1263027{col 28}{space 2} .0762046{col 39}{space 1}    1.66{col 48}{space 3}0.097{col 56}{space 4}-.0230557{col 69}{space 3}  .275661
{txt}{space 11}Age {c |}{col 16}{res}{space 2} .0022035{col 28}{space 2} .0059325{col 39}{space 1}    0.37{col 48}{space 3}0.710{col 56}{space 4}-.0094241{col 69}{space 3} .0138311
{txt}{space 9}Urban {c |}{col 16}{res}{space 2} .0918806{col 28}{space 2} .1335204{col 39}{space 1}    0.69{col 48}{space 3}0.491{col 56}{space 4}-.1698146{col 69}{space 3} .3535759
{txt}{space 8}Hunger {c |}{col 16}{res}{space 2}-.1389583{col 28}{space 2} .0950765{col 39}{space 1}   -1.46{col 48}{space 3}0.144{col 56}{space 4}-.3253048{col 69}{space 3} .0473882
{txt}{space 10}Male {c |}{col 16}{res}{space 2} .0212077{col 28}{space 2} .1649776{col 39}{space 1}    0.13{col 48}{space 3}0.898{col 56}{space 4}-.3021425{col 69}{space 3} .3445578
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-.8414803{col 28}{space 2} .6293482{col 56}{space 4} -2.07498{col 69}{space 3} .3920195
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2} .9924554{col 28}{space 2} .5814699{col 56}{space 4}-.1472046{col 69}{space 3} 2.132115
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est1{txt} stored)

{com}. ologit CollectiveA_2 3.religion climateworsening $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-749.95104}  
Iteration 1:{space 3}log pseudolikelihood = {res: -709.2691}  
Iteration 2:{space 3}log pseudolikelihood = {res:-708.78055}  
Iteration 3:{space 3}log pseudolikelihood = {res:-708.77991}  
Iteration 4:{space 3}log pseudolikelihood = {res:-708.77991}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       730
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-708.77991{txt}{col 49}Pseudo R2{col 67}= {res}    0.0549

{txt}{ralign 82:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}   CollectiveA_2{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}religion {c |}
{space 9}Muslim  {c |}{col 18}{res}{space 2}-1.129582{col 30}{space 2} .3693032{col 41}{space 1}   -3.06{col 50}{space 3}0.002{col 58}{space 4}-1.853402{col 71}{space 3}-.4057606
{txt}climateworsening {c |}{col 18}{res}{space 2}-.1234103{col 30}{space 2} .0670584{col 41}{space 1}   -1.84{col 50}{space 3}0.066{col 58}{space 4}-.2548423{col 71}{space 3} .0080216
{txt}{space 11}masai {c |}{col 18}{res}{space 2} -.456551{col 30}{space 2} .2944143{col 41}{space 1}   -1.55{col 50}{space 3}0.121{col 58}{space 4}-1.033592{col 71}{space 3} .1204904
{txt}{space 10}somali {c |}{col 18}{res}{space 2} .6678401{col 30}{space 2} .4970505{col 41}{space 1}    1.34{col 50}{space 3}0.179{col 58}{space 4}-.3063609{col 71}{space 3} 1.642041
{txt}{space 4}pastoralists {c |}{col 18}{res}{space 2}-.6063041{col 30}{space 2} .2012614{col 41}{space 1}   -3.01{col 50}{space 3}0.003{col 58}{space 4}-1.000769{col 71}{space 3}-.2118389
{txt}{space 16} {c |}
{space 10}region {c |}
{space 8}Central  {c |}{col 18}{res}{space 2} .4507391{col 30}{space 2} .0531582{col 41}{space 1}    8.48{col 50}{space 3}0.000{col 58}{space 4} .3465509{col 71}{space 3} .5549272
{txt}{space 8}Eastern  {c |}{col 18}{res}{space 2} .3895028{col 30}{space 2} .0904422{col 41}{space 1}    4.31{col 50}{space 3}0.000{col 58}{space 4} .2122394{col 71}{space 3} .5667662
{txt}{space 4}Rift Valley  {c |}{col 18}{res}{space 2} .4542281{col 30}{space 2}  .083041{col 41}{space 1}    5.47{col 50}{space 3}0.000{col 58}{space 4} .2914707{col 71}{space 3} .6169855
{txt}{space 9}Nyanza  {c |}{col 18}{res}{space 2} .6622814{col 30}{space 2} .0886819{col 41}{space 1}    7.47{col 50}{space 3}0.000{col 58}{space 4} .4884681{col 71}{space 3} .8360948
{txt}{space 8}Western  {c |}{col 18}{res}{space 2}-.4275541{col 30}{space 2} .1339401{col 41}{space 1}   -3.19{col 50}{space 3}0.001{col 58}{space 4}-.6900718{col 71}{space 3}-.1650363
{txt}{space 2}North Eastern  {c |}{col 18}{res}{space 2}-.5457441{col 30}{space 2} .2663446{col 41}{space 1}   -2.05{col 50}{space 3}0.040{col 58}{space 4} -1.06777{col 71}{space 3}-.0237182
{txt}{space 10}Coast  {c |}{col 18}{res}{space 2}-.3086101{col 30}{space 2} .1888258{col 41}{space 1}   -1.63{col 50}{space 3}0.102{col 58}{space 4}-.6787018{col 71}{space 3} .0614816
{txt}{space 16} {c |}
{space 7}Education {c |}{col 18}{res}{space 2} .1192576{col 30}{space 2} .0723691{col 41}{space 1}    1.65{col 50}{space 3}0.099{col 58}{space 4}-.0225832{col 71}{space 3} .2610984
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0009926{col 30}{space 2} .0049596{col 41}{space 1}    0.20{col 50}{space 3}0.841{col 58}{space 4}-.0087281{col 71}{space 3} .0107132
{txt}{space 11}Urban {c |}{col 18}{res}{space 2} .1011427{col 30}{space 2} .1405464{col 41}{space 1}    0.72{col 50}{space 3}0.472{col 58}{space 4}-.1743233{col 71}{space 3} .3766086
{txt}{space 10}Hunger {c |}{col 18}{res}{space 2}-.1259287{col 30}{space 2} .0940696{col 41}{space 1}   -1.34{col 50}{space 3}0.181{col 58}{space 4}-.3103018{col 71}{space 3} .0584444
{txt}{space 12}Male {c |}{col 18}{res}{space 2} .0328804{col 30}{space 2} .1609027{col 41}{space 1}    0.20{col 50}{space 3}0.838{col 58}{space 4} -.282483{col 71}{space 3} .3482439
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}  -.96634{col 30}{space 2} .6129152{col 58}{space 4}-2.167632{col 71}{space 3} .2349518
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2} .8461943{col 30}{space 2} .5379033{col 58}{space 4}-.2080768{col 71}{space 3} 1.900465
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est2{txt} stored)

{com}. ologit CollectiveA_2 3.religion climateworsening BothHumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-728.41983}  
Iteration 1:{space 3}log pseudolikelihood = {res:-676.03112}  
Iteration 2:{space 3}log pseudolikelihood = {res:-675.12362}  
Iteration 3:{space 3}log pseudolikelihood = {res:-675.12142}  
Iteration 4:{space 3}log pseudolikelihood = {res:-675.12142}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       718
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-675.12142{txt}{col 49}Pseudo R2{col 67}= {res}    0.0732

{txt}{ralign 82:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}   CollectiveA_2{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}religion {c |}
{space 9}Muslim  {c |}{col 18}{res}{space 2}-.9669602{col 30}{space 2} .3519165{col 41}{space 1}   -2.75{col 50}{space 3}0.006{col 58}{space 4}-1.656704{col 71}{space 3}-.2772165
{txt}climateworsening {c |}{col 18}{res}{space 2}-.1991495{col 30}{space 2} .0736187{col 41}{space 1}   -2.71{col 50}{space 3}0.007{col 58}{space 4}-.3434396{col 71}{space 3}-.0548594
{txt}{space 6}BothHumanA {c |}{col 18}{res}{space 2} 1.143245{col 30}{space 2} .3889195{col 41}{space 1}    2.94{col 50}{space 3}0.003{col 58}{space 4}  .380977{col 71}{space 3} 1.905513
{txt}{space 11}masai {c |}{col 18}{res}{space 2}-.5994797{col 30}{space 2} .3340824{col 41}{space 1}   -1.79{col 50}{space 3}0.073{col 58}{space 4}-1.254269{col 71}{space 3} .0553097
{txt}{space 10}somali {c |}{col 18}{res}{space 2} .1252036{col 30}{space 2}  .364108{col 41}{space 1}    0.34{col 50}{space 3}0.731{col 58}{space 4} -.588435{col 71}{space 3} .8388422
{txt}{space 4}pastoralists {c |}{col 18}{res}{space 2}-.6435664{col 30}{space 2} .2702826{col 41}{space 1}   -2.38{col 50}{space 3}0.017{col 58}{space 4} -1.17331{col 71}{space 3}-.1138223
{txt}{space 16} {c |}
{space 10}region {c |}
{space 8}Central  {c |}{col 18}{res}{space 2} .6265592{col 30}{space 2} .1070531{col 41}{space 1}    5.85{col 50}{space 3}0.000{col 58}{space 4}  .416739{col 71}{space 3} .8363794
{txt}{space 8}Eastern  {c |}{col 18}{res}{space 2} .6210586{col 30}{space 2} .1799409{col 41}{space 1}    3.45{col 50}{space 3}0.001{col 58}{space 4} .2683808{col 71}{space 3} .9737363
{txt}{space 4}Rift Valley  {c |}{col 18}{res}{space 2} .4443393{col 30}{space 2}  .110257{col 41}{space 1}    4.03{col 50}{space 3}0.000{col 58}{space 4} .2282396{col 71}{space 3} .6604389
{txt}{space 9}Nyanza  {c |}{col 18}{res}{space 2} .6783511{col 30}{space 2} .1293235{col 41}{space 1}    5.25{col 50}{space 3}0.000{col 58}{space 4} .4248817{col 71}{space 3} .9318206
{txt}{space 8}Western  {c |}{col 18}{res}{space 2} -.479286{col 30}{space 2} .1359999{col 41}{space 1}   -3.52{col 50}{space 3}0.000{col 58}{space 4}-.7458408{col 71}{space 3}-.2127312
{txt}{space 2}North Eastern  {c |}{col 18}{res}{space 2}-.0418616{col 30}{space 2} .2412374{col 41}{space 1}   -0.17{col 50}{space 3}0.862{col 58}{space 4}-.5146781{col 71}{space 3} .4309549
{txt}{space 10}Coast  {c |}{col 18}{res}{space 2}-.0551416{col 30}{space 2} .2118133{col 41}{space 1}   -0.26{col 50}{space 3}0.795{col 58}{space 4} -.470288{col 71}{space 3} .3600048
{txt}{space 16} {c |}
{space 7}Education {c |}{col 18}{res}{space 2} .0847517{col 30}{space 2} .0644796{col 41}{space 1}    1.31{col 50}{space 3}0.189{col 58}{space 4} -.041626{col 71}{space 3} .2111294
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0015361{col 30}{space 2} .0047127{col 41}{space 1}    0.33{col 50}{space 3}0.744{col 58}{space 4}-.0077006{col 71}{space 3} .0107728
{txt}{space 11}Urban {c |}{col 18}{res}{space 2}  .091327{col 30}{space 2} .1591433{col 41}{space 1}    0.57{col 50}{space 3}0.566{col 58}{space 4}-.2205881{col 71}{space 3} .4032422
{txt}{space 10}Hunger {c |}{col 18}{res}{space 2}-.1333724{col 30}{space 2} .0908967{col 41}{space 1}   -1.47{col 50}{space 3}0.142{col 58}{space 4}-.3115267{col 71}{space 3} .0447818
{txt}{space 12}Male {c |}{col 18}{res}{space 2} .0746821{col 30}{space 2} .1787351{col 41}{space 1}    0.42{col 50}{space 3}0.676{col 58}{space 4}-.2756322{col 71}{space 3} .4249965
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-.1954109{col 30}{space 2} .8701174{col 58}{space 4} -1.90081{col 71}{space 3} 1.509988
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}  1.72215{col 30}{space 2} .8358829{col 58}{space 4} .0838496{col 71}{space 3}  3.36045
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est3{txt} stored)

{com}. ologit CollectiveA_2 c.statetrust##3.religion climateworsening BothHumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-728.41983}  
Iteration 1:{space 3}log pseudolikelihood = {res:-675.12189}  
Iteration 2:{space 3}log pseudolikelihood = {res:-674.22211}  
Iteration 3:{space 3}log pseudolikelihood = {res:-674.21989}  
Iteration 4:{space 3}log pseudolikelihood = {res:-674.21989}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       718
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-674.21989{txt}{col 49}Pseudo R2{col 67}= {res}    0.0744

{txt}{ralign 87:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}        CollectiveA_2{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}statetrust {c |}{col 23}{res}{space 2}-.0943293{col 35}{space 2} .4916774{col 46}{space 1}   -0.19{col 55}{space 3}0.848{col 63}{space 4}-1.057999{col 76}{space 3} .8693406
{txt}{space 21} {c |}
{space 13}religion {c |}
{space 14}Muslim  {c |}{col 23}{res}{space 2}-1.981969{col 35}{space 2} .5632923{col 46}{space 1}   -3.52{col 55}{space 3}0.000{col 63}{space 4}-3.086001{col 76}{space 3}-.8779359
{txt}{space 21} {c |}
religion#c.statetrust {c |}
{space 14}Muslim  {c |}{col 23}{res}{space 2} 1.982713{col 35}{space 2} .5427644{col 46}{space 1}    3.65{col 55}{space 3}0.000{col 63}{space 4} .9189144{col 76}{space 3} 3.046512
{txt}{space 21} {c |}
{space 5}climateworsening {c |}{col 23}{res}{space 2}-.2013274{col 35}{space 2} .0648632{col 46}{space 1}   -3.10{col 55}{space 3}0.002{col 63}{space 4} -.328457{col 76}{space 3}-.0741978
{txt}{space 11}BothHumanA {c |}{col 23}{res}{space 2} 1.165453{col 35}{space 2} .3984735{col 46}{space 1}    2.92{col 55}{space 3}0.003{col 63}{space 4} .3844597{col 76}{space 3} 1.946447
{txt}{space 16}masai {c |}{col 23}{res}{space 2}-.5832766{col 35}{space 2} .3390713{col 46}{space 1}   -1.72{col 55}{space 3}0.085{col 63}{space 4}-1.247844{col 76}{space 3} .0812909
{txt}{space 15}somali {c |}{col 23}{res}{space 2}-.3821555{col 35}{space 2} .4545447{col 46}{space 1}   -0.84{col 55}{space 3}0.400{col 63}{space 4}-1.273047{col 76}{space 3} .5087358
{txt}{space 9}pastoralists {c |}{col 23}{res}{space 2}-.6480075{col 35}{space 2}  .264912{col 46}{space 1}   -2.45{col 55}{space 3}0.014{col 63}{space 4}-1.167226{col 76}{space 3}-.1287895
{txt}{space 21} {c |}
{space 15}region {c |}
{space 13}Central  {c |}{col 23}{res}{space 2} .6431365{col 35}{space 2} .1039129{col 46}{space 1}    6.19{col 55}{space 3}0.000{col 63}{space 4} .4394709{col 76}{space 3} .8468022
{txt}{space 13}Eastern  {c |}{col 23}{res}{space 2} .6398077{col 35}{space 2} .1803897{col 46}{space 1}    3.55{col 55}{space 3}0.000{col 63}{space 4} .2862504{col 76}{space 3}  .993365
{txt}{space 9}Rift Valley  {c |}{col 23}{res}{space 2} .4548337{col 35}{space 2}  .106702{col 46}{space 1}    4.26{col 55}{space 3}0.000{col 63}{space 4} .2457016{col 76}{space 3} .6639658
{txt}{space 14}Nyanza  {c |}{col 23}{res}{space 2}  .677017{col 35}{space 2} .1341319{col 46}{space 1}    5.05{col 55}{space 3}0.000{col 63}{space 4} .4141234{col 76}{space 3} .9399106
{txt}{space 13}Western  {c |}{col 23}{res}{space 2}-.4737118{col 35}{space 2} .1319648{col 46}{space 1}   -3.59{col 55}{space 3}0.000{col 63}{space 4}-.7323581{col 76}{space 3}-.2150656
{txt}{space 7}North Eastern  {c |}{col 23}{res}{space 2} .2881909{col 35}{space 2}  .343602{col 46}{space 1}    0.84{col 55}{space 3}0.402{col 63}{space 4}-.3852566{col 76}{space 3} .9616385
{txt}{space 15}Coast  {c |}{col 23}{res}{space 2}-.0545711{col 35}{space 2} .2158432{col 46}{space 1}   -0.25{col 55}{space 3}0.800{col 63}{space 4} -.477616{col 76}{space 3} .3684739
{txt}{space 21} {c |}
{space 12}Education {c |}{col 23}{res}{space 2} .0831397{col 35}{space 2} .0633795{col 46}{space 1}    1.31{col 55}{space 3}0.190{col 63}{space 4}-.0410818{col 76}{space 3} .2073612
{txt}{space 18}Age {c |}{col 23}{res}{space 2} .0018627{col 35}{space 2} .0050251{col 46}{space 1}    0.37{col 55}{space 3}0.711{col 63}{space 4}-.0079863{col 76}{space 3} .0117118
{txt}{space 16}Urban {c |}{col 23}{res}{space 2} .1078835{col 35}{space 2} .1460456{col 46}{space 1}    0.74{col 55}{space 3}0.460{col 63}{space 4}-.1783607{col 76}{space 3} .3941277
{txt}{space 15}Hunger {c |}{col 23}{res}{space 2}-.1304667{col 35}{space 2} .0969584{col 46}{space 1}   -1.35{col 55}{space 3}0.178{col 63}{space 4}-.3205017{col 76}{space 3} .0595684
{txt}{space 17}Male {c |}{col 23}{res}{space 2} .0697876{col 35}{space 2} .1794783{col 46}{space 1}    0.39{col 55}{space 3}0.697{col 63}{space 4}-.2819834{col 76}{space 3} .4215586
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}/cut1 {c |}{col 23}{res}{space 2}-.2090883{col 35}{space 2} .9028576{col 63}{space 4}-1.978657{col 76}{space 3}  1.56048
{txt}{space 16}/cut2 {c |}{col 23}{res}{space 2} 1.712713{col 35}{space 2} .8709457{col 63}{space 4} .0056905{col 76}{space 3} 3.419735
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est4{txt} stored)

{com}. ologit CollectiveA_2 c.trustgap##3.religion climateworsening BothHumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-721.32583}  
Iteration 1:{space 3}log pseudolikelihood = {res:-663.90793}  
Iteration 2:{space 3}log pseudolikelihood = {res:-662.94295}  
Iteration 3:{space 3}log pseudolikelihood = {res:-662.94036}  
Iteration 4:{space 3}log pseudolikelihood = {res:-662.94036}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       710
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-662.94036{txt}{col 49}Pseudo R2{col 67}= {res}    0.0809

{txt}{ralign 85:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}      CollectiveA_2{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}trustgap {c |}{col 21}{res}{space 2}-.0148989{col 33}{space 2} .3386428{col 44}{space 1}   -0.04{col 53}{space 3}0.965{col 61}{space 4}-.6786265{col 74}{space 3} .6488287
{txt}{space 19} {c |}
{space 11}religion {c |}
{space 12}Muslim  {c |}{col 21}{res}{space 2}-.0643314{col 33}{space 2} .3536326{col 44}{space 1}   -0.18{col 53}{space 3}0.856{col 61}{space 4}-.7574385{col 74}{space 3} .6287757
{txt}{space 19} {c |}
religion#c.trustgap {c |}
{space 12}Muslim  {c |}{col 21}{res}{space 2}-2.866921{col 33}{space 2} .9901917{col 44}{space 1}   -2.90{col 53}{space 3}0.004{col 61}{space 4}-4.807661{col 74}{space 3}-.9261811
{txt}{space 19} {c |}
{space 3}climateworsening {c |}{col 21}{res}{space 2}-.1945988{col 33}{space 2} .0679927{col 44}{space 1}   -2.86{col 53}{space 3}0.004{col 61}{space 4} -.327862{col 74}{space 3}-.0613356
{txt}{space 9}BothHumanA {c |}{col 21}{res}{space 2} 1.168449{col 33}{space 2}   .42792{col 44}{space 1}    2.73{col 53}{space 3}0.006{col 61}{space 4} .3297415{col 74}{space 3} 2.007157
{txt}{space 14}masai {c |}{col 21}{res}{space 2}-.5853869{col 33}{space 2} .3459476{col 44}{space 1}   -1.69{col 53}{space 3}0.091{col 61}{space 4}-1.263432{col 74}{space 3}  .092658
{txt}{space 13}somali {c |}{col 21}{res}{space 2}-.3303542{col 33}{space 2} .3926791{col 44}{space 1}   -0.84{col 53}{space 3}0.400{col 61}{space 4}-1.099991{col 74}{space 3} .4392827
{txt}{space 7}pastoralists {c |}{col 21}{res}{space 2}-.6680088{col 33}{space 2} .2617535{col 44}{space 1}   -2.55{col 53}{space 3}0.011{col 61}{space 4}-1.181036{col 74}{space 3}-.1549813
{txt}{space 19} {c |}
{space 13}region {c |}
{space 11}Central  {c |}{col 21}{res}{space 2} .7287732{col 33}{space 2} .1189208{col 44}{space 1}    6.13{col 53}{space 3}0.000{col 61}{space 4} .4956927{col 74}{space 3} .9618537
{txt}{space 11}Eastern  {c |}{col 21}{res}{space 2} .7466011{col 33}{space 2} .1856443{col 44}{space 1}    4.02{col 53}{space 3}0.000{col 61}{space 4}  .382745{col 74}{space 3} 1.110457
{txt}{space 7}Rift Valley  {c |}{col 21}{res}{space 2} .5552942{col 33}{space 2} .1126841{col 44}{space 1}    4.93{col 53}{space 3}0.000{col 61}{space 4} .3344374{col 74}{space 3} .7761509
{txt}{space 12}Nyanza  {c |}{col 21}{res}{space 2} .7868627{col 33}{space 2}  .124793{col 44}{space 1}    6.31{col 53}{space 3}0.000{col 61}{space 4} .5422729{col 74}{space 3} 1.031453
{txt}{space 11}Western  {c |}{col 21}{res}{space 2}-.4590488{col 33}{space 2} .1349129{col 44}{space 1}   -3.40{col 53}{space 3}0.001{col 61}{space 4}-.7234732{col 74}{space 3}-.1946245
{txt}{space 5}North Eastern  {c |}{col 21}{res}{space 2}  .650766{col 33}{space 2} .3607042{col 44}{space 1}    1.80{col 53}{space 3}0.071{col 61}{space 4}-.0562012{col 74}{space 3} 1.357733
{txt}{space 13}Coast  {c |}{col 21}{res}{space 2}-.0368089{col 33}{space 2} .2212854{col 44}{space 1}   -0.17{col 53}{space 3}0.868{col 61}{space 4}-.4705204{col 74}{space 3} .3969025
{txt}{space 19} {c |}
{space 10}Education {c |}{col 21}{res}{space 2} .0861638{col 33}{space 2} .0647868{col 44}{space 1}    1.33{col 53}{space 3}0.184{col 61}{space 4} -.040816{col 74}{space 3} .2131435
{txt}{space 16}Age {c |}{col 21}{res}{space 2} .0014935{col 33}{space 2} .0047304{col 44}{space 1}    0.32{col 53}{space 3}0.752{col 61}{space 4} -.007778{col 74}{space 3}  .010765
{txt}{space 14}Urban {c |}{col 21}{res}{space 2} .1367733{col 33}{space 2} .1189794{col 44}{space 1}    1.15{col 53}{space 3}0.250{col 61}{space 4} -.096422{col 74}{space 3} .3699687
{txt}{space 13}Hunger {c |}{col 21}{res}{space 2}-.1326201{col 33}{space 2} .0949572{col 44}{space 1}   -1.40{col 53}{space 3}0.163{col 61}{space 4}-.3187328{col 74}{space 3} .0534925
{txt}{space 15}Male {c |}{col 21}{res}{space 2}  .084364{col 33}{space 2} .1786634{col 44}{space 1}    0.47{col 53}{space 3}0.637{col 61}{space 4}-.2658098{col 74}{space 3} .4345379
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}/cut1 {c |}{col 21}{res}{space 2}-.0621689{col 33}{space 2} .8727753{col 61}{space 4}-1.772777{col 74}{space 3} 1.648439
{txt}{space 14}/cut2 {c |}{col 21}{res}{space 2} 1.874154{col 33}{space 2} .8417267{col 61}{space 4} .2244002{col 74}{space 3} 3.523908
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est5{txt} stored)

{com}. esttab, replace b(3) se(3) label star(* .10 ** .05 *** .01) nogaps nomtitles nodepvars nopar
{res}
{txt}{hline 100}
{txt}                              (1)             (2)             (3)             (4)             (5)   
{txt}{hline 100}
{res}Q78: Ordinary Keny~t                                                                                {txt}
{txt}Muslim              {res}       -0.917*         -1.130***       -0.967***       -1.982***       -0.064   {txt}
                    {res}        0.485           0.369           0.352           0.563           0.354   {txt}
{txt}Masai/Samburu       {res}       -0.445          -0.457          -0.599*         -0.583*         -0.585*  {txt}
                    {res}        0.305           0.294           0.334           0.339           0.346   {txt}
{txt}Somali              {res}        0.324           0.668           0.125          -0.382          -0.330   {txt}
                    {res}        0.631           0.497           0.364           0.455           0.393   {txt}
{txt}Pastoralist         {res}       -0.631***       -0.606***       -0.644**        -0.648**        -0.668** {txt}
                    {res}        0.209           0.201           0.270           0.265           0.262   {txt}
{txt}Nairobi             {res}        0.000           0.000           0.000           0.000           0.000   {txt}
                    {res}            .               .               .               .               .   {txt}
{txt}Central             {res}        0.463***        0.451***        0.627***        0.643***        0.729***{txt}
                    {res}        0.062           0.053           0.107           0.104           0.119   {txt}
{txt}Eastern             {res}        0.387***        0.390***        0.621***        0.640***        0.747***{txt}
                    {res}        0.098           0.090           0.180           0.180           0.186   {txt}
{txt}Rift Valley         {res}        0.494***        0.454***        0.444***        0.455***        0.555***{txt}
                    {res}        0.090           0.083           0.110           0.107           0.113   {txt}
{txt}Nyanza              {res}        0.629***        0.662***        0.678***        0.677***        0.787***{txt}
                    {res}        0.103           0.089           0.129           0.134           0.125   {txt}
{txt}Western             {res}       -0.423***       -0.428***       -0.479***       -0.474***       -0.459***{txt}
                    {res}        0.139           0.134           0.136           0.132           0.135   {txt}
{txt}North Eastern       {res}       -0.430*         -0.546**        -0.042           0.288           0.651*  {txt}
                    {res}        0.235           0.266           0.241           0.344           0.361   {txt}
{txt}Coast               {res}       -0.380*         -0.309          -0.055          -0.055          -0.037   {txt}
                    {res}        0.209           0.189           0.212           0.216           0.221   {txt}
{txt}Education           {res}        0.126*          0.119*          0.085           0.083           0.086   {txt}
                    {res}        0.076           0.072           0.064           0.063           0.065   {txt}
{txt}Age                 {res}        0.002           0.001           0.002           0.002           0.001   {txt}
                    {res}        0.006           0.005           0.005           0.005           0.005   {txt}
{txt}Urban               {res}        0.092           0.101           0.091           0.108           0.137   {txt}
                    {res}        0.134           0.141           0.159           0.146           0.119   {txt}
{txt}Food shortage freq~y{res}       -0.139          -0.126          -0.133          -0.130          -0.133   {txt}
                    {res}        0.095           0.094           0.091           0.097           0.095   {txt}
{txt}Male                {res}        0.021           0.033           0.075           0.070           0.084   {txt}
                    {res}        0.165           0.161           0.179           0.179           0.179   {txt}
{txt}Climate Worsening   {res}                       -0.123*         -0.199***       -0.201***       -0.195***{txt}
                    {res}                        0.067           0.074           0.065           0.068   {txt}
{txt}Q75: dummy for any~m{res}                                        1.143***        1.165***        1.168***{txt}
                    {res}                                        0.389           0.398           0.428   {txt}
{txt}Trust in State      {res}                                                       -0.094                   {txt}
                    {res}                                                        0.492                   {txt}
{txt}Muslim # Trust in ~e{res}                                                        1.983***                {txt}
                    {res}                                                        0.543                   {txt}
{txt}Trust Gap (Re.. St~){res}                                                                       -0.015   {txt}
                    {res}                                                                        0.339   {txt}
{txt}Muslim # Trus.. St~){res}                                                                       -2.867***{txt}
                    {res}                                                                        0.990   {txt}
{hline 100}
{res}/                                                                                                   {txt}
{txt}cut1                {res}       -0.841          -0.966          -0.195          -0.209          -0.062   {txt}
                    {res}        0.629           0.613           0.870           0.903           0.873   {txt}
{txt}cut2                {res}        0.992*          0.846           1.722**         1.713**         1.874** {txt}
                    {res}        0.581           0.538           0.836           0.871           0.842   {txt}
{txt}{hline 100}
{txt}Observations        {res}          743             730             718             718             710   {txt}
{txt}{hline 100}
{txt}Standard errors in second row
{txt}* p<.10, ** p<.05, *** p<.01

{com}. *esttab using Table1, tex replace b(3) se(3) label star(* .10 ** .05 *** .01) nogaps nomtitles nodepvars
. 
. 
. ***** in text discussion: causes of climate change ************************************************************************
. *HumanA = Only anthropogenic
. *BothHumanA = Anthropogenic + natural causes
. mean HumanA [pweight= withinwt]
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}       988

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 6}HumanA {c |}{col 14}{res}{space 2} .6136835{col 26}{space 2} .0169001{col 37}{space 5} .5805192{col 51}{space 3} .6468478
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. mean BothHumanA [pweight= withinwt]
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}       988

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 2}BothHumanA {c |}{col 14}{res}{space 2}  .749544{col 26}{space 2} .0151403{col 37}{space 5} .7198332{col 51}{space 3} .7792547
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. 
. ****** in text discussion: predicted probabilities ************************************************************************
. global indvars masai somali pastoralists i.region Education Age Urban Hunger Male 
{txt}
{com}. global modelspecs [pweight= withinwt], cluster(region)
{txt}
{com}. ologit CollectiveA_2 c.statetrust##Musl climateworsening BothHumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-729.10574}  
Iteration 1:{space 3}log pseudolikelihood = {res:-675.76153}  
Iteration 2:{space 3}log pseudolikelihood = {res: -674.8585}  
Iteration 3:{space 3}log pseudolikelihood = {res:-674.85627}  
Iteration 4:{space 3}log pseudolikelihood = {res:-674.85627}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       719
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-674.85627{txt}{col 49}Pseudo R2{col 67}= {res}    0.0744

{txt}{ralign 83:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}    CollectiveA_2{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}statetrust {c |}{col 19}{res}{space 2}-.1122993{col 31}{space 2} .4934546{col 42}{space 1}   -0.23{col 51}{space 3}0.820{col 59}{space 4}-1.079453{col 72}{space 3} .8548539
{txt}{space 11}1.Musl {c |}{col 19}{res}{space 2}-1.995981{col 31}{space 2} .5683094{col 42}{space 1}   -3.51{col 51}{space 3}0.000{col 59}{space 4}-3.109847{col 72}{space 3}-.8821149
{txt}{space 17} {c |}
Musl#c.statetrust {c |}
{space 15}1  {c |}{col 19}{res}{space 2}  2.00291{col 31}{space 2} .5468994{col 42}{space 1}    3.66{col 51}{space 3}0.000{col 59}{space 4}  .931007{col 72}{space 3} 3.074813
{txt}{space 17} {c |}
{space 1}climateworsening {c |}{col 19}{res}{space 2}-.2006646{col 31}{space 2} .0655745{col 42}{space 1}   -3.06{col 51}{space 3}0.002{col 59}{space 4}-.3291883{col 72}{space 3}-.0721409
{txt}{space 7}BothHumanA {c |}{col 19}{res}{space 2} 1.168028{col 31}{space 2} .3983484{col 42}{space 1}    2.93{col 51}{space 3}0.003{col 59}{space 4} .3872795{col 72}{space 3} 1.948777
{txt}{space 12}masai {c |}{col 19}{res}{space 2}-.5819566{col 31}{space 2}  .339281{col 42}{space 1}   -1.72{col 51}{space 3}0.086{col 59}{space 4}-1.246935{col 72}{space 3} .0830218
{txt}{space 11}somali {c |}{col 19}{res}{space 2}-.3780377{col 31}{space 2} .4564369{col 42}{space 1}   -0.83{col 51}{space 3}0.408{col 59}{space 4}-1.272638{col 72}{space 3} .5165622
{txt}{space 5}pastoralists {c |}{col 19}{res}{space 2}-.6506742{col 31}{space 2} .2652377{col 42}{space 1}   -2.45{col 51}{space 3}0.014{col 59}{space 4}-1.170531{col 72}{space 3}-.1308178
{txt}{space 17} {c |}
{space 11}region {c |}
{space 9}Central  {c |}{col 19}{res}{space 2} .6429929{col 31}{space 2} .1044617{col 42}{space 1}    6.16{col 51}{space 3}0.000{col 59}{space 4} .4382517{col 72}{space 3} .8477341
{txt}{space 9}Eastern  {c |}{col 19}{res}{space 2}  .636961{col 31}{space 2} .1803622{col 42}{space 1}    3.53{col 51}{space 3}0.000{col 59}{space 4} .2834576{col 72}{space 3} .9904644
{txt}{space 5}Rift Valley  {c |}{col 19}{res}{space 2} .4530826{col 31}{space 2} .1066911{col 42}{space 1}    4.25{col 51}{space 3}0.000{col 59}{space 4} .2439718{col 72}{space 3} .6621934
{txt}{space 10}Nyanza  {c |}{col 19}{res}{space 2} .6916232{col 31}{space 2} .1340918{col 42}{space 1}    5.16{col 51}{space 3}0.000{col 59}{space 4} .4288082{col 72}{space 3} .9544382
{txt}{space 9}Western  {c |}{col 19}{res}{space 2}-.4791626{col 31}{space 2} .1319532{col 42}{space 1}   -3.63{col 51}{space 3}0.000{col 59}{space 4}-.7377862{col 72}{space 3} -.220539
{txt}{space 3}North Eastern  {c |}{col 19}{res}{space 2} .2852597{col 31}{space 2} .3452422{col 42}{space 1}    0.83{col 51}{space 3}0.409{col 59}{space 4}-.3914026{col 72}{space 3} .9619219
{txt}{space 11}Coast  {c |}{col 19}{res}{space 2}-.0553291{col 31}{space 2} .2162068{col 42}{space 1}   -0.26{col 51}{space 3}0.798{col 59}{space 4}-.4790866{col 72}{space 3} .3684284
{txt}{space 17} {c |}
{space 8}Education {c |}{col 19}{res}{space 2} .0828243{col 31}{space 2} .0634059{col 42}{space 1}    1.31{col 51}{space 3}0.191{col 59}{space 4} -.041449{col 72}{space 3} .2070975
{txt}{space 14}Age {c |}{col 19}{res}{space 2}  .001748{col 31}{space 2} .0050435{col 42}{space 1}    0.35{col 51}{space 3}0.729{col 59}{space 4}-.0081371{col 72}{space 3} .0116331
{txt}{space 12}Urban {c |}{col 19}{res}{space 2} .1027112{col 31}{space 2} .1453942{col 42}{space 1}    0.71{col 51}{space 3}0.480{col 59}{space 4}-.1822562{col 72}{space 3} .3876785
{txt}{space 11}Hunger {c |}{col 19}{res}{space 2}-.1253137{col 31}{space 2} .0966129{col 42}{space 1}   -1.30{col 51}{space 3}0.195{col 59}{space 4}-.3146714{col 72}{space 3} .0640441
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0750669{col 31}{space 2} .1746823{col 42}{space 1}    0.43{col 51}{space 3}0.667{col 59}{space 4}-.2673042{col 72}{space 3} .4174381
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-.2184692{col 31}{space 2} .9033265{col 59}{space 4}-1.988957{col 72}{space 3} 1.552018
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2} 1.702004{col 31}{space 2}  .870073{col 59}{space 4}-.0033074{col 72}{space 3} 3.407316
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         margins, at(Musl=(0 1) statetrust=(0(.2)1) ) 
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       719
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._predict}:{space 1}{res:Pr(CollectiveA_2==0), predict(pr outcome(0))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._predict}:{space 1}{res:Pr(CollectiveA_2==1), predict(pr outcome(1))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._predict}:{space 1}{res:Pr(CollectiveA_2==2), predict(pr outcome(2))}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.4}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.4}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.6}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.6}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:9._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.8}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:10._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.8}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:11._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:12._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_predict#_at {c |}
{space 7}1  1  {c |}{col 14}{res}{space 2} .1610699{col 26}{space 2} .0378761{col 37}{space 1}    4.25{col 46}{space 3}0.000{col 54}{space 4} .0868342{col 67}{space 3} .2353056
{txt}{space 7}1  2  {c |}{col 14}{res}{space 2} .5402096{col 26}{space 2} .1064379{col 37}{space 1}    5.08{col 46}{space 3}0.000{col 54}{space 4} .3315952{col 67}{space 3}  .748824
{txt}{space 7}1  3  {c |}{col 14}{res}{space 2} .1638931{col 26}{space 2} .0272698{col 37}{space 1}    6.01{col 46}{space 3}0.000{col 54}{space 4} .1104452{col 67}{space 3}  .217341
{txt}{space 7}1  4  {c |}{col 14}{res}{space 2} .4548094{col 26}{space 2} .0973297{col 37}{space 1}    4.67{col 46}{space 3}0.000{col 54}{space 4} .2640467{col 67}{space 3} .6455721
{txt}{space 7}1  5  {c |}{col 14}{res}{space 2} .1667539{col 26}{space 2} .0183263{col 37}{space 1}    9.10{col 46}{space 3}0.000{col 54}{space 4} .1308351{col 67}{space 3} .2026727
{txt}{space 7}1  6  {c |}{col 14}{res}{space 2} .3723272{col 26}{space 2} .0852125{col 37}{space 1}    4.37{col 46}{space 3}0.000{col 54}{space 4} .2053138{col 67}{space 3} .5393406
{txt}{space 7}1  7  {c |}{col 14}{res}{space 2} .1696525{col 26}{space 2} .0153536{col 37}{space 1}   11.05{col 46}{space 3}0.000{col 54}{space 4}   .13956{col 67}{space 3} .1997449
{txt}{space 7}1  8  {c |}{col 14}{res}{space 2} .2966031{col 26}{space 2} .0721218{col 37}{space 1}    4.11{col 46}{space 3}0.000{col 54}{space 4} .1552469{col 67}{space 3} .4379592
{txt}{space 7}1  9  {c |}{col 14}{res}{space 2} .1725889{col 26}{space 2} .0216482{col 37}{space 1}    7.97{col 46}{space 3}0.000{col 54}{space 4} .1301592{col 67}{space 3} .2150187
{txt}{space 7}1 10  {c |}{col 14}{res}{space 2} .2302826{col 26}{space 2} .0596725{col 37}{space 1}    3.86{col 46}{space 3}0.000{col 54}{space 4} .1133266{col 67}{space 3} .3472386
{txt}{space 7}1 11  {c |}{col 14}{res}{space 2} .1755634{col 26}{space 2} .0325698{col 37}{space 1}    5.39{col 46}{space 3}0.000{col 54}{space 4} .1117277{col 67}{space 3} .2393991
{txt}{space 7}1 12  {c |}{col 14}{res}{space 2} .1746417{col 26}{space 2} .0487681{col 37}{space 1}    3.58{col 46}{space 3}0.000{col 54}{space 4} .0790579{col 67}{space 3} .2702255
{txt}{space 7}2  1  {c |}{col 14}{res}{space 2} .3620653{col 26}{space 2} .0360794{col 37}{space 1}   10.04{col 46}{space 3}0.000{col 54}{space 4}  .291351{col 67}{space 3} .4327797
{txt}{space 7}2  2  {c |}{col 14}{res}{space 2}  .336569{col 26}{space 2} .0686614{col 37}{space 1}    4.90{col 46}{space 3}0.000{col 54}{space 4} .2019951{col 67}{space 3}  .471143
{txt}{space 7}2  3  {c |}{col 14}{res}{space 2} .3643254{col 26}{space 2} .0302691{col 37}{space 1}   12.04{col 46}{space 3}0.000{col 54}{space 4}  .304999{col 67}{space 3} .4236518
{txt}{space 7}2  4  {c |}{col 14}{res}{space 2} .3769939{col 26}{space 2} .0553226{col 37}{space 1}    6.81{col 46}{space 3}0.000{col 54}{space 4} .2685636{col 67}{space 3} .4854243
{txt}{space 7}2  5  {c |}{col 14}{res}{space 2} .3665445{col 26}{space 2} .0273401{col 37}{space 1}   13.41{col 46}{space 3}0.000{col 54}{space 4}  .312959{col 67}{space 3} .4201301
{txt}{space 7}2  6  {c |}{col 14}{res}{space 2} .4030515{col 26}{space 2} .0415356{col 37}{space 1}    9.70{col 46}{space 3}0.000{col 54}{space 4} .3216432{col 67}{space 3} .4844599
{txt}{space 7}2  7  {c |}{col 14}{res}{space 2} .3687216{col 26}{space 2} .0278243{col 37}{space 1}   13.25{col 46}{space 3}0.000{col 54}{space 4} .3141871{col 67}{space 3} .4232562
{txt}{space 7}2  8  {c |}{col 14}{res}{space 2} .4110768{col 26}{space 2} .0322809{col 37}{space 1}   12.73{col 46}{space 3}0.000{col 54}{space 4} .3478075{col 67}{space 3} .4743461
{txt}{space 7}2  9  {c |}{col 14}{res}{space 2} .3708555{col 26}{space 2} .0312199{col 37}{space 1}   11.88{col 46}{space 3}0.000{col 54}{space 4} .3096657{col 67}{space 3} .4320454
{txt}{space 7}2 10  {c |}{col 14}{res}{space 2} .4001605{col 26}{space 2} .0322126{col 37}{space 1}   12.42{col 46}{space 3}0.000{col 54}{space 4} .3370249{col 67}{space 3}  .463296
{txt}{space 7}2 11  {c |}{col 14}{res}{space 2} .3729452{col 26}{space 2}  .036429{col 37}{space 1}   10.24{col 46}{space 3}0.000{col 54}{space 4} .3015457{col 67}{space 3} .4443446
{txt}{space 7}2 12  {c |}{col 14}{res}{space 2} .3723053{col 26}{space 2} .0391089{col 37}{space 1}    9.52{col 46}{space 3}0.000{col 54}{space 4} .2956532{col 67}{space 3} .4489574
{txt}{space 7}3  1  {c |}{col 14}{res}{space 2} .4768648{col 26}{space 2} .0606948{col 37}{space 1}    7.86{col 46}{space 3}0.000{col 54}{space 4} .3579051{col 67}{space 3} .5958245
{txt}{space 7}3  2  {c |}{col 14}{res}{space 2} .1232214{col 26}{space 2} .0422114{col 37}{space 1}    2.92{col 46}{space 3}0.004{col 54}{space 4} .0404884{col 67}{space 3} .2059543
{txt}{space 7}3  3  {c |}{col 14}{res}{space 2} .4717815{col 26}{space 2} .0391328{col 37}{space 1}   12.06{col 46}{space 3}0.000{col 54}{space 4} .3950826{col 67}{space 3} .5484804
{txt}{space 7}3  4  {c |}{col 14}{res}{space 2} .1681967{col 26}{space 2} .0492633{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .0716423{col 67}{space 3}  .264751
{txt}{space 7}3  5  {c |}{col 14}{res}{space 2} .4667016{col 26}{space 2} .0195488{col 37}{space 1}   23.87{col 46}{space 3}0.000{col 54}{space 4} .4283866{col 67}{space 3} .5050165
{txt}{space 7}3  6  {c |}{col 14}{res}{space 2} .2246212{col 26}{space 2} .0562172{col 37}{space 1}    4.00{col 46}{space 3}0.000{col 54}{space 4} .1144376{col 67}{space 3} .3348049
{txt}{space 7}3  7  {c |}{col 14}{res}{space 2} .4616259{col 26}{space 2} .0152634{col 37}{space 1}   30.24{col 46}{space 3}0.000{col 54}{space 4} .4317102{col 67}{space 3} .4915417
{txt}{space 7}3  8  {c |}{col 14}{res}{space 2} .2923201{col 26}{space 2} .0629734{col 37}{space 1}    4.64{col 46}{space 3}0.000{col 54}{space 4} .1688945{col 67}{space 3} .4157457
{txt}{space 7}3  9  {c |}{col 14}{res}{space 2} .4565555{col 26}{space 2} .0328374{col 37}{space 1}   13.90{col 46}{space 3}0.000{col 54}{space 4} .3921954{col 67}{space 3} .5209157
{txt}{space 7}3 10  {c |}{col 14}{res}{space 2}  .369557{col 26}{space 2} .0694205{col 37}{space 1}    5.32{col 46}{space 3}0.000{col 54}{space 4} .2334953{col 67}{space 3} .5056187
{txt}{space 7}3 11  {c |}{col 14}{res}{space 2} .4514914{col 26}{space 2} .0539473{col 37}{space 1}    8.37{col 46}{space 3}0.000{col 54}{space 4} .3457566{col 67}{space 3} .5572262
{txt}{space 7}3 12  {c |}{col 14}{res}{space 2}  .453053{col 26}{space 2} .0751577{col 37}{space 1}    6.03{col 46}{space 3}0.000{col 54}{space 4} .3057466{col 67}{space 3} .6003595
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. ologit CollectiveA_2 c.trustgap##Musl climateworsening BothHumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-722.01525}  
Iteration 1:{space 3}log pseudolikelihood = {res:-664.56999}  
Iteration 2:{space 3}log pseudolikelihood = {res:-663.60151}  
Iteration 3:{space 3}log pseudolikelihood = {res: -663.5989}  
Iteration 4:{space 3}log pseudolikelihood = {res: -663.5989}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       711
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res} -663.5989{txt}{col 49}Pseudo R2{col 67}= {res}    0.0809

{txt}{ralign 82:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}   CollectiveA_2{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}trustgap {c |}{col 18}{res}{space 2} .0034811{col 30}{space 2} .3386736{col 41}{space 1}    0.01{col 50}{space 3}0.992{col 58}{space 4}-.6603069{col 71}{space 3} .6672691
{txt}{space 10}1.Musl {c |}{col 18}{res}{space 2}-.0645156{col 30}{space 2} .3529411{col 41}{space 1}   -0.18{col 50}{space 3}0.855{col 58}{space 4}-.7562674{col 71}{space 3} .6272363
{txt}{space 16} {c |}
{space 1}Musl#c.trustgap {c |}
{space 14}1  {c |}{col 18}{res}{space 2}-2.882325{col 30}{space 2} .9901157{col 41}{space 1}   -2.91{col 50}{space 3}0.004{col 58}{space 4}-4.822916{col 71}{space 3}-.9417334
{txt}{space 16} {c |}
climateworsening {c |}{col 18}{res}{space 2}-.1936065{col 30}{space 2} .0687409{col 41}{space 1}   -2.82{col 50}{space 3}0.005{col 58}{space 4}-.3283362{col 71}{space 3}-.0588767
{txt}{space 6}BothHumanA {c |}{col 18}{res}{space 2} 1.169587{col 30}{space 2} .4270029{col 41}{space 1}    2.74{col 50}{space 3}0.006{col 58}{space 4} .3326767{col 71}{space 3} 2.006497
{txt}{space 11}masai {c |}{col 18}{res}{space 2}-.5813812{col 30}{space 2} .3478114{col 41}{space 1}   -1.67{col 50}{space 3}0.095{col 58}{space 4}-1.263079{col 71}{space 3} .1003165
{txt}{space 10}somali {c |}{col 18}{res}{space 2}-.3254969{col 30}{space 2} .3956014{col 41}{space 1}   -0.82{col 50}{space 3}0.411{col 58}{space 4}-1.100861{col 71}{space 3} .4498676
{txt}{space 4}pastoralists {c |}{col 18}{res}{space 2}-.6707835{col 30}{space 2} .2627111{col 41}{space 1}   -2.55{col 50}{space 3}0.011{col 58}{space 4}-1.185688{col 71}{space 3}-.1558792
{txt}{space 16} {c |}
{space 10}region {c |}
{space 8}Central  {c |}{col 18}{res}{space 2} .7260468{col 30}{space 2} .1185083{col 41}{space 1}    6.13{col 50}{space 3}0.000{col 58}{space 4} .4937748{col 71}{space 3} .9583187
{txt}{space 8}Eastern  {c |}{col 18}{res}{space 2} .7408855{col 30}{space 2} .1845728{col 41}{space 1}    4.01{col 50}{space 3}0.000{col 58}{space 4} .3791294{col 71}{space 3} 1.102642
{txt}{space 4}Rift Valley  {c |}{col 18}{res}{space 2} .5508552{col 30}{space 2} .1118653{col 41}{space 1}    4.92{col 50}{space 3}0.000{col 58}{space 4} .3316033{col 71}{space 3} .7701071
{txt}{space 9}Nyanza  {c |}{col 18}{res}{space 2} .8013812{col 30}{space 2} .1244368{col 41}{space 1}    6.44{col 50}{space 3}0.000{col 58}{space 4} .5574896{col 71}{space 3} 1.045273
{txt}{space 8}Western  {c |}{col 18}{res}{space 2}-.4680828{col 30}{space 2}  .134877{col 41}{space 1}   -3.47{col 50}{space 3}0.001{col 58}{space 4}-.7324369{col 71}{space 3}-.2037288
{txt}{space 2}North Eastern  {c |}{col 18}{res}{space 2} .6443786{col 30}{space 2} .3595896{col 41}{space 1}    1.79{col 50}{space 3}0.073{col 58}{space 4}-.0604041{col 71}{space 3} 1.349161
{txt}{space 10}Coast  {c |}{col 18}{res}{space 2}-.0413355{col 30}{space 2} .2206094{col 41}{space 1}   -0.19{col 50}{space 3}0.851{col 58}{space 4} -.473722{col 71}{space 3} .3910509
{txt}{space 16} {c |}
{space 7}Education {c |}{col 18}{res}{space 2}  .085789{col 30}{space 2} .0648062{col 41}{space 1}    1.32{col 50}{space 3}0.186{col 58}{space 4}-.0412288{col 71}{space 3} .2128069
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0013235{col 30}{space 2} .0047481{col 41}{space 1}    0.28{col 50}{space 3}0.780{col 58}{space 4}-.0079826{col 71}{space 3} .0106296
{txt}{space 11}Urban {c |}{col 18}{res}{space 2} .1323702{col 30}{space 2} .1182254{col 41}{space 1}    1.12{col 50}{space 3}0.263{col 58}{space 4}-.0993474{col 71}{space 3} .3640878
{txt}{space 10}Hunger {c |}{col 18}{res}{space 2}-.1266885{col 30}{space 2} .0952289{col 41}{space 1}   -1.33{col 50}{space 3}0.183{col 58}{space 4}-.3133337{col 71}{space 3} .0599566
{txt}{space 12}Male {c |}{col 18}{res}{space 2} .0907768{col 30}{space 2} .1732085{col 41}{space 1}    0.52{col 50}{space 3}0.600{col 58}{space 4}-.2487056{col 71}{space 3} .4302592
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-.0619963{col 30}{space 2} .8737922{col 58}{space 4}-1.774598{col 71}{space 3} 1.650605
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2} 1.872857{col 30}{space 2} .8420641{col 58}{space 4} .2224412{col 71}{space 3} 3.523272
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         margins, at(Musl=(1) trustgap=(-1(.25)1) ) 
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       711
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._predict}:{space 1}{res:Pr(CollectiveA_2==0), predict(pr outcome(0))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._predict}:{space 1}{res:Pr(CollectiveA_2==1), predict(pr outcome(1))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._predict}:{space 1}{res:Pr(CollectiveA_2==2), predict(pr outcome(2))}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 9}-1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 7}-.75}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 8}-.5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 7}-.25}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 8}.25}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 9}.5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 8}.75}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:9._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_predict#_at {c |}
{space 8}1 1  {c |}{col 14}{res}{space 2} .0132236{col 26}{space 2}  .015661{col 37}{space 1}    0.84{col 46}{space 3}0.398{col 54}{space 4}-.0174713{col 67}{space 3} .0439185
{txt}{space 8}1 2  {c |}{col 14}{res}{space 2} .0265169{col 26}{space 2} .0243492{col 37}{space 1}    1.09{col 46}{space 3}0.276{col 54}{space 4}-.0212067{col 67}{space 3} .0742405
{txt}{space 8}1 3  {c |}{col 14}{res}{space 2} .0520366{col 26}{space 2} .0343857{col 37}{space 1}    1.51{col 46}{space 3}0.130{col 54}{space 4}-.0153581{col 67}{space 3} .1194314
{txt}{space 8}1 4  {c |}{col 14}{res}{space 2}  .098444{col 26}{space 2} .0426797{col 37}{space 1}    2.31{col 46}{space 3}0.021{col 54}{space 4} .0147934{col 67}{space 3} .1820946
{txt}{space 8}1 5  {c |}{col 14}{res}{space 2} .1760898{col 26}{space 2}  .050095{col 37}{space 1}    3.52{col 46}{space 3}0.000{col 54}{space 4} .0779053{col 67}{space 3} .2742743
{txt}{space 8}1 6  {c |}{col 14}{res}{space 2} .2917881{col 26}{space 2} .0759731{col 37}{space 1}    3.84{col 46}{space 3}0.000{col 54}{space 4} .1428835{col 67}{space 3} .4406927
{txt}{space 8}1 7  {c |}{col 14}{res}{space 2} .4406364{col 26}{space 2} .1281867{col 37}{space 1}    3.44{col 46}{space 3}0.001{col 54}{space 4}  .189395{col 67}{space 3} .6918778
{txt}{space 8}1 8  {c |}{col 14}{res}{space 2}  .601781{col 26}{space 2} .1756431{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4}  .257527{col 67}{space 3} .9460351
{txt}{space 8}1 9  {c |}{col 14}{res}{space 2} .7461198{col 26}{space 2} .1856396{col 37}{space 1}    4.02{col 46}{space 3}0.000{col 54}{space 4} .3822728{col 67}{space 3} 1.109967
{txt}{space 8}2 1  {c |}{col 14}{res}{space 2} .0678902{col 26}{space 2} .0706847{col 37}{space 1}    0.96{col 46}{space 3}0.337{col 54}{space 4}-.0706493{col 67}{space 3} .2064296
{txt}{space 8}2 2  {c |}{col 14}{res}{space 2} .1214601{col 26}{space 2} .0882131{col 37}{space 1}    1.38{col 46}{space 3}0.169{col 54}{space 4}-.0514344{col 67}{space 3} .2943546
{txt}{space 8}2 3  {c |}{col 14}{res}{space 2} .1995971{col 26}{space 2}  .087994{col 37}{space 1}    2.27{col 46}{space 3}0.023{col 54}{space 4}  .027132{col 67}{space 3} .3720622
{txt}{space 8}2 4  {c |}{col 14}{res}{space 2} .2932185{col 26}{space 2} .0640554{col 37}{space 1}    4.58{col 46}{space 3}0.000{col 54}{space 4} .1676722{col 67}{space 3} .4187647
{txt}{space 8}2 5  {c |}{col 14}{res}{space 2} .3759205{col 26}{space 2}  .035414{col 37}{space 1}   10.62{col 46}{space 3}0.000{col 54}{space 4} .3065103{col 67}{space 3} .4453308
{txt}{space 8}2 6  {c |}{col 14}{res}{space 2} .4127302{col 26}{space 2} .0320649{col 37}{space 1}   12.87{col 46}{space 3}0.000{col 54}{space 4} .3498842{col 67}{space 3} .4755761
{txt}{space 8}2 7  {c |}{col 14}{res}{space 2} .3837101{col 26}{space 2}  .063345{col 37}{space 1}    6.06{col 46}{space 3}0.000{col 54}{space 4} .2595562{col 67}{space 3} .5078641
{txt}{space 8}2 8  {c |}{col 14}{res}{space 2} .3019513{col 26}{space 2} .1143852{col 37}{space 1}    2.64{col 46}{space 3}0.008{col 54}{space 4} .0777605{col 67}{space 3} .5261421
{txt}{space 8}2 9  {c |}{col 14}{res}{space 2} .2038748{col 26}{space 2} .1397625{col 37}{space 1}    1.46{col 46}{space 3}0.145{col 54}{space 4}-.0700546{col 67}{space 3} .4778043
{txt}{space 8}3 1  {c |}{col 14}{res}{space 2} .9188862{col 26}{space 2} .0861613{col 37}{space 1}   10.66{col 46}{space 3}0.000{col 54}{space 4} .7500132{col 67}{space 3} 1.087759
{txt}{space 8}3 2  {c |}{col 14}{res}{space 2}  .852023{col 26}{space 2} .1120939{col 37}{space 1}    7.60{col 46}{space 3}0.000{col 54}{space 4} .6323231{col 67}{space 3} 1.071723
{txt}{space 8}3 3  {c |}{col 14}{res}{space 2} .7483663{col 26}{space 2} .1210834{col 37}{space 1}    6.18{col 46}{space 3}0.000{col 54}{space 4} .5110472{col 67}{space 3} .9856854
{txt}{space 8}3 4  {c |}{col 14}{res}{space 2} .6083376{col 26}{space 2} .1026871{col 37}{space 1}    5.92{col 46}{space 3}0.000{col 54}{space 4} .4070745{col 67}{space 3} .8096006
{txt}{space 8}3 5  {c |}{col 14}{res}{space 2} .4479896{col 26}{space 2} .0714798{col 37}{space 1}    6.27{col 46}{space 3}0.000{col 54}{space 4} .3078918{col 67}{space 3} .5880875
{txt}{space 8}3 6  {c |}{col 14}{res}{space 2} .2954817{col 26}{space 2} .0656157{col 37}{space 1}    4.50{col 46}{space 3}0.000{col 54}{space 4} .1668774{col 67}{space 3} .4240861
{txt}{space 8}3 7  {c |}{col 14}{res}{space 2} .1756535{col 26}{space 2} .0716211{col 37}{space 1}    2.45{col 46}{space 3}0.014{col 54}{space 4} .0352787{col 67}{space 3} .3160283
{txt}{space 8}3 8  {c |}{col 14}{res}{space 2} .0962677{col 26}{space 2} .0634947{col 37}{space 1}    1.52{col 46}{space 3}0.129{col 54}{space 4}-.0281796{col 67}{space 3} .2207149
{txt}{space 8}3 9  {c |}{col 14}{res}{space 2} .0500054{col 26}{space 2}  .046712{col 37}{space 1}    1.07{col 46}{space 3}0.284{col 54}{space 4}-.0415484{col 67}{space 3} .1415593
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************* Figure 2 ************************************************************************
. global indvars masai somali pastoralists i.region Education Age Urban Hunger Male 
{txt}
{com}. global modelspecs [pweight= withinwt], cluster(region)
{txt}
{com}. ologit CollectiveA_2 c.statetrust##Musl climateworsening BothHumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-729.10574}  
Iteration 1:{space 3}log pseudolikelihood = {res:-675.76153}  
Iteration 2:{space 3}log pseudolikelihood = {res: -674.8585}  
Iteration 3:{space 3}log pseudolikelihood = {res:-674.85627}  
Iteration 4:{space 3}log pseudolikelihood = {res:-674.85627}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       719
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-674.85627{txt}{col 49}Pseudo R2{col 67}= {res}    0.0744

{txt}{ralign 83:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}    CollectiveA_2{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}statetrust {c |}{col 19}{res}{space 2}-.1122993{col 31}{space 2} .4934546{col 42}{space 1}   -0.23{col 51}{space 3}0.820{col 59}{space 4}-1.079453{col 72}{space 3} .8548539
{txt}{space 11}1.Musl {c |}{col 19}{res}{space 2}-1.995981{col 31}{space 2} .5683094{col 42}{space 1}   -3.51{col 51}{space 3}0.000{col 59}{space 4}-3.109847{col 72}{space 3}-.8821149
{txt}{space 17} {c |}
Musl#c.statetrust {c |}
{space 15}1  {c |}{col 19}{res}{space 2}  2.00291{col 31}{space 2} .5468994{col 42}{space 1}    3.66{col 51}{space 3}0.000{col 59}{space 4}  .931007{col 72}{space 3} 3.074813
{txt}{space 17} {c |}
{space 1}climateworsening {c |}{col 19}{res}{space 2}-.2006646{col 31}{space 2} .0655745{col 42}{space 1}   -3.06{col 51}{space 3}0.002{col 59}{space 4}-.3291883{col 72}{space 3}-.0721409
{txt}{space 7}BothHumanA {c |}{col 19}{res}{space 2} 1.168028{col 31}{space 2} .3983484{col 42}{space 1}    2.93{col 51}{space 3}0.003{col 59}{space 4} .3872795{col 72}{space 3} 1.948777
{txt}{space 12}masai {c |}{col 19}{res}{space 2}-.5819566{col 31}{space 2}  .339281{col 42}{space 1}   -1.72{col 51}{space 3}0.086{col 59}{space 4}-1.246935{col 72}{space 3} .0830218
{txt}{space 11}somali {c |}{col 19}{res}{space 2}-.3780377{col 31}{space 2} .4564369{col 42}{space 1}   -0.83{col 51}{space 3}0.408{col 59}{space 4}-1.272638{col 72}{space 3} .5165622
{txt}{space 5}pastoralists {c |}{col 19}{res}{space 2}-.6506742{col 31}{space 2} .2652377{col 42}{space 1}   -2.45{col 51}{space 3}0.014{col 59}{space 4}-1.170531{col 72}{space 3}-.1308178
{txt}{space 17} {c |}
{space 11}region {c |}
{space 9}Central  {c |}{col 19}{res}{space 2} .6429929{col 31}{space 2} .1044617{col 42}{space 1}    6.16{col 51}{space 3}0.000{col 59}{space 4} .4382517{col 72}{space 3} .8477341
{txt}{space 9}Eastern  {c |}{col 19}{res}{space 2}  .636961{col 31}{space 2} .1803622{col 42}{space 1}    3.53{col 51}{space 3}0.000{col 59}{space 4} .2834576{col 72}{space 3} .9904644
{txt}{space 5}Rift Valley  {c |}{col 19}{res}{space 2} .4530826{col 31}{space 2} .1066911{col 42}{space 1}    4.25{col 51}{space 3}0.000{col 59}{space 4} .2439718{col 72}{space 3} .6621934
{txt}{space 10}Nyanza  {c |}{col 19}{res}{space 2} .6916232{col 31}{space 2} .1340918{col 42}{space 1}    5.16{col 51}{space 3}0.000{col 59}{space 4} .4288082{col 72}{space 3} .9544382
{txt}{space 9}Western  {c |}{col 19}{res}{space 2}-.4791626{col 31}{space 2} .1319532{col 42}{space 1}   -3.63{col 51}{space 3}0.000{col 59}{space 4}-.7377862{col 72}{space 3} -.220539
{txt}{space 3}North Eastern  {c |}{col 19}{res}{space 2} .2852597{col 31}{space 2} .3452422{col 42}{space 1}    0.83{col 51}{space 3}0.409{col 59}{space 4}-.3914026{col 72}{space 3} .9619219
{txt}{space 11}Coast  {c |}{col 19}{res}{space 2}-.0553291{col 31}{space 2} .2162068{col 42}{space 1}   -0.26{col 51}{space 3}0.798{col 59}{space 4}-.4790866{col 72}{space 3} .3684284
{txt}{space 17} {c |}
{space 8}Education {c |}{col 19}{res}{space 2} .0828243{col 31}{space 2} .0634059{col 42}{space 1}    1.31{col 51}{space 3}0.191{col 59}{space 4} -.041449{col 72}{space 3} .2070975
{txt}{space 14}Age {c |}{col 19}{res}{space 2}  .001748{col 31}{space 2} .0050435{col 42}{space 1}    0.35{col 51}{space 3}0.729{col 59}{space 4}-.0081371{col 72}{space 3} .0116331
{txt}{space 12}Urban {c |}{col 19}{res}{space 2} .1027112{col 31}{space 2} .1453942{col 42}{space 1}    0.71{col 51}{space 3}0.480{col 59}{space 4}-.1822562{col 72}{space 3} .3876785
{txt}{space 11}Hunger {c |}{col 19}{res}{space 2}-.1253137{col 31}{space 2} .0966129{col 42}{space 1}   -1.30{col 51}{space 3}0.195{col 59}{space 4}-.3146714{col 72}{space 3} .0640441
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0750669{col 31}{space 2} .1746823{col 42}{space 1}    0.43{col 51}{space 3}0.667{col 59}{space 4}-.2673042{col 72}{space 3} .4174381
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-.2184692{col 31}{space 2} .9033265{col 59}{space 4}-1.988957{col 72}{space 3} 1.552018
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2} 1.702004{col 31}{space 2}  .870073{col 59}{space 4}-.0033074{col 72}{space 3} 3.407316
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. preserve
{txt}
{com}.         margins, at(Musl=(0 1) statetrust=(0(.2)1) ) post 
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       719
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._predict}:{space 1}{res:Pr(CollectiveA_2==0), predict(pr outcome(0))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._predict}:{space 1}{res:Pr(CollectiveA_2==1), predict(pr outcome(1))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._predict}:{space 1}{res:Pr(CollectiveA_2==2), predict(pr outcome(2))}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.4}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.4}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.6}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.6}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:9._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.8}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:10._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 9}.8}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:11._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:12._at}:{space 1}{res:{txt:statetrust}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_predict#_at {c |}
{space 7}1  1  {c |}{col 14}{res}{space 2} .1610699{col 26}{space 2} .0378761{col 37}{space 1}    4.25{col 46}{space 3}0.000{col 54}{space 4} .0868342{col 67}{space 3} .2353056
{txt}{space 7}1  2  {c |}{col 14}{res}{space 2} .5402096{col 26}{space 2} .1064379{col 37}{space 1}    5.08{col 46}{space 3}0.000{col 54}{space 4} .3315952{col 67}{space 3}  .748824
{txt}{space 7}1  3  {c |}{col 14}{res}{space 2} .1638931{col 26}{space 2} .0272698{col 37}{space 1}    6.01{col 46}{space 3}0.000{col 54}{space 4} .1104452{col 67}{space 3}  .217341
{txt}{space 7}1  4  {c |}{col 14}{res}{space 2} .4548094{col 26}{space 2} .0973297{col 37}{space 1}    4.67{col 46}{space 3}0.000{col 54}{space 4} .2640467{col 67}{space 3} .6455721
{txt}{space 7}1  5  {c |}{col 14}{res}{space 2} .1667539{col 26}{space 2} .0183263{col 37}{space 1}    9.10{col 46}{space 3}0.000{col 54}{space 4} .1308351{col 67}{space 3} .2026727
{txt}{space 7}1  6  {c |}{col 14}{res}{space 2} .3723272{col 26}{space 2} .0852125{col 37}{space 1}    4.37{col 46}{space 3}0.000{col 54}{space 4} .2053138{col 67}{space 3} .5393406
{txt}{space 7}1  7  {c |}{col 14}{res}{space 2} .1696525{col 26}{space 2} .0153536{col 37}{space 1}   11.05{col 46}{space 3}0.000{col 54}{space 4}   .13956{col 67}{space 3} .1997449
{txt}{space 7}1  8  {c |}{col 14}{res}{space 2} .2966031{col 26}{space 2} .0721218{col 37}{space 1}    4.11{col 46}{space 3}0.000{col 54}{space 4} .1552469{col 67}{space 3} .4379592
{txt}{space 7}1  9  {c |}{col 14}{res}{space 2} .1725889{col 26}{space 2} .0216482{col 37}{space 1}    7.97{col 46}{space 3}0.000{col 54}{space 4} .1301592{col 67}{space 3} .2150187
{txt}{space 7}1 10  {c |}{col 14}{res}{space 2} .2302826{col 26}{space 2} .0596725{col 37}{space 1}    3.86{col 46}{space 3}0.000{col 54}{space 4} .1133266{col 67}{space 3} .3472386
{txt}{space 7}1 11  {c |}{col 14}{res}{space 2} .1755634{col 26}{space 2} .0325698{col 37}{space 1}    5.39{col 46}{space 3}0.000{col 54}{space 4} .1117277{col 67}{space 3} .2393991
{txt}{space 7}1 12  {c |}{col 14}{res}{space 2} .1746417{col 26}{space 2} .0487681{col 37}{space 1}    3.58{col 46}{space 3}0.000{col 54}{space 4} .0790579{col 67}{space 3} .2702255
{txt}{space 7}2  1  {c |}{col 14}{res}{space 2} .3620653{col 26}{space 2} .0360794{col 37}{space 1}   10.04{col 46}{space 3}0.000{col 54}{space 4}  .291351{col 67}{space 3} .4327797
{txt}{space 7}2  2  {c |}{col 14}{res}{space 2}  .336569{col 26}{space 2} .0686614{col 37}{space 1}    4.90{col 46}{space 3}0.000{col 54}{space 4} .2019951{col 67}{space 3}  .471143
{txt}{space 7}2  3  {c |}{col 14}{res}{space 2} .3643254{col 26}{space 2} .0302691{col 37}{space 1}   12.04{col 46}{space 3}0.000{col 54}{space 4}  .304999{col 67}{space 3} .4236518
{txt}{space 7}2  4  {c |}{col 14}{res}{space 2} .3769939{col 26}{space 2} .0553226{col 37}{space 1}    6.81{col 46}{space 3}0.000{col 54}{space 4} .2685636{col 67}{space 3} .4854243
{txt}{space 7}2  5  {c |}{col 14}{res}{space 2} .3665445{col 26}{space 2} .0273401{col 37}{space 1}   13.41{col 46}{space 3}0.000{col 54}{space 4}  .312959{col 67}{space 3} .4201301
{txt}{space 7}2  6  {c |}{col 14}{res}{space 2} .4030515{col 26}{space 2} .0415356{col 37}{space 1}    9.70{col 46}{space 3}0.000{col 54}{space 4} .3216432{col 67}{space 3} .4844599
{txt}{space 7}2  7  {c |}{col 14}{res}{space 2} .3687216{col 26}{space 2} .0278243{col 37}{space 1}   13.25{col 46}{space 3}0.000{col 54}{space 4} .3141871{col 67}{space 3} .4232562
{txt}{space 7}2  8  {c |}{col 14}{res}{space 2} .4110768{col 26}{space 2} .0322809{col 37}{space 1}   12.73{col 46}{space 3}0.000{col 54}{space 4} .3478075{col 67}{space 3} .4743461
{txt}{space 7}2  9  {c |}{col 14}{res}{space 2} .3708555{col 26}{space 2} .0312199{col 37}{space 1}   11.88{col 46}{space 3}0.000{col 54}{space 4} .3096657{col 67}{space 3} .4320454
{txt}{space 7}2 10  {c |}{col 14}{res}{space 2} .4001605{col 26}{space 2} .0322126{col 37}{space 1}   12.42{col 46}{space 3}0.000{col 54}{space 4} .3370249{col 67}{space 3}  .463296
{txt}{space 7}2 11  {c |}{col 14}{res}{space 2} .3729452{col 26}{space 2}  .036429{col 37}{space 1}   10.24{col 46}{space 3}0.000{col 54}{space 4} .3015457{col 67}{space 3} .4443446
{txt}{space 7}2 12  {c |}{col 14}{res}{space 2} .3723053{col 26}{space 2} .0391089{col 37}{space 1}    9.52{col 46}{space 3}0.000{col 54}{space 4} .2956532{col 67}{space 3} .4489574
{txt}{space 7}3  1  {c |}{col 14}{res}{space 2} .4768648{col 26}{space 2} .0606948{col 37}{space 1}    7.86{col 46}{space 3}0.000{col 54}{space 4} .3579051{col 67}{space 3} .5958245
{txt}{space 7}3  2  {c |}{col 14}{res}{space 2} .1232214{col 26}{space 2} .0422114{col 37}{space 1}    2.92{col 46}{space 3}0.004{col 54}{space 4} .0404884{col 67}{space 3} .2059543
{txt}{space 7}3  3  {c |}{col 14}{res}{space 2} .4717815{col 26}{space 2} .0391328{col 37}{space 1}   12.06{col 46}{space 3}0.000{col 54}{space 4} .3950826{col 67}{space 3} .5484804
{txt}{space 7}3  4  {c |}{col 14}{res}{space 2} .1681967{col 26}{space 2} .0492633{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .0716423{col 67}{space 3}  .264751
{txt}{space 7}3  5  {c |}{col 14}{res}{space 2} .4667016{col 26}{space 2} .0195488{col 37}{space 1}   23.87{col 46}{space 3}0.000{col 54}{space 4} .4283866{col 67}{space 3} .5050165
{txt}{space 7}3  6  {c |}{col 14}{res}{space 2} .2246212{col 26}{space 2} .0562172{col 37}{space 1}    4.00{col 46}{space 3}0.000{col 54}{space 4} .1144376{col 67}{space 3} .3348049
{txt}{space 7}3  7  {c |}{col 14}{res}{space 2} .4616259{col 26}{space 2} .0152634{col 37}{space 1}   30.24{col 46}{space 3}0.000{col 54}{space 4} .4317102{col 67}{space 3} .4915417
{txt}{space 7}3  8  {c |}{col 14}{res}{space 2} .2923201{col 26}{space 2} .0629734{col 37}{space 1}    4.64{col 46}{space 3}0.000{col 54}{space 4} .1688945{col 67}{space 3} .4157457
{txt}{space 7}3  9  {c |}{col 14}{res}{space 2} .4565555{col 26}{space 2} .0328374{col 37}{space 1}   13.90{col 46}{space 3}0.000{col 54}{space 4} .3921954{col 67}{space 3} .5209157
{txt}{space 7}3 10  {c |}{col 14}{res}{space 2}  .369557{col 26}{space 2} .0694205{col 37}{space 1}    5.32{col 46}{space 3}0.000{col 54}{space 4} .2334953{col 67}{space 3} .5056187
{txt}{space 7}3 11  {c |}{col 14}{res}{space 2} .4514914{col 26}{space 2} .0539473{col 37}{space 1}    8.37{col 46}{space 3}0.000{col 54}{space 4} .3457566{col 67}{space 3} .5572262
{txt}{space 7}3 12  {c |}{col 14}{res}{space 2}  .453053{col 26}{space 2} .0751577{col 37}{space 1}    6.03{col 46}{space 3}0.000{col 54}{space 4} .3057466{col 67}{space 3} .6003595
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         parmest, label norestore 
{res}{txt}
{com}.         egen muslim = seq(), f(0) t(1)
{txt}
{com}.                 lab def muslim 0 "Non-Muslims" 1 "Muslims"
{txt}
{com}.                 lab val muslim muslim
{txt}
{com}.         egen religioustrust = seq(), f(0) t(5) block(2)
{txt}
{com}.                 replace religioustrust = religioustrust/5
{txt}variable {bf}religioustrust{sf} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(30 real changes made)

{com}.         egen outcome = seq(), f(1) t(3) block(12)
{txt}
{com}.         graph twoway (rcap min95 max95 religioustrust if outcome != 2, lcolor(gs10)) ///
>                          (connected estimate religioustrust if outcome == 1, lcolor(gs5) mcolor(gs5) msymbol(D)) ///
>                          (connected estimate religioustrust if outcome == 3, lcolor(gs10) lwidth(thick) lpattern(dash_dot) mcolor(gs10) msymbol(O)), ///
>                 by(muslim, graphregion(fcolor(white) lcolor(black)) note(" ")           )       ///
>                 ytitle("Predicted Agency Beliefs") xtitle("Trust in State Leaders") xlabel(0 "None" 1 "A lot") yscale(r(0 1)) ///
>                 legend(order(2 "Can do nothing" 3 "Can do a lot")) ylabel(0(.2)1)
{res}{txt}
{com}. restore
{txt}
{com}. 
. ******************************* Figure 3 ************************************************************************
. global indvars masai somali pastoralists i.region Education Age Urban Hunger Male 
{txt}
{com}. global modelspecs [pweight= withinwt], cluster(region)
{txt}
{com}. ologit CollectiveA_2 c.trustgap##Musl climateworsening BothHumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-722.01525}  
Iteration 1:{space 3}log pseudolikelihood = {res:-664.56999}  
Iteration 2:{space 3}log pseudolikelihood = {res:-663.60151}  
Iteration 3:{space 3}log pseudolikelihood = {res: -663.5989}  
Iteration 4:{space 3}log pseudolikelihood = {res: -663.5989}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       711
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res} -663.5989{txt}{col 49}Pseudo R2{col 67}= {res}    0.0809

{txt}{ralign 82:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}   CollectiveA_2{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}trustgap {c |}{col 18}{res}{space 2} .0034811{col 30}{space 2} .3386736{col 41}{space 1}    0.01{col 50}{space 3}0.992{col 58}{space 4}-.6603069{col 71}{space 3} .6672691
{txt}{space 10}1.Musl {c |}{col 18}{res}{space 2}-.0645156{col 30}{space 2} .3529411{col 41}{space 1}   -0.18{col 50}{space 3}0.855{col 58}{space 4}-.7562674{col 71}{space 3} .6272363
{txt}{space 16} {c |}
{space 1}Musl#c.trustgap {c |}
{space 14}1  {c |}{col 18}{res}{space 2}-2.882325{col 30}{space 2} .9901157{col 41}{space 1}   -2.91{col 50}{space 3}0.004{col 58}{space 4}-4.822916{col 71}{space 3}-.9417334
{txt}{space 16} {c |}
climateworsening {c |}{col 18}{res}{space 2}-.1936065{col 30}{space 2} .0687409{col 41}{space 1}   -2.82{col 50}{space 3}0.005{col 58}{space 4}-.3283362{col 71}{space 3}-.0588767
{txt}{space 6}BothHumanA {c |}{col 18}{res}{space 2} 1.169587{col 30}{space 2} .4270029{col 41}{space 1}    2.74{col 50}{space 3}0.006{col 58}{space 4} .3326767{col 71}{space 3} 2.006497
{txt}{space 11}masai {c |}{col 18}{res}{space 2}-.5813812{col 30}{space 2} .3478114{col 41}{space 1}   -1.67{col 50}{space 3}0.095{col 58}{space 4}-1.263079{col 71}{space 3} .1003165
{txt}{space 10}somali {c |}{col 18}{res}{space 2}-.3254969{col 30}{space 2} .3956014{col 41}{space 1}   -0.82{col 50}{space 3}0.411{col 58}{space 4}-1.100861{col 71}{space 3} .4498676
{txt}{space 4}pastoralists {c |}{col 18}{res}{space 2}-.6707835{col 30}{space 2} .2627111{col 41}{space 1}   -2.55{col 50}{space 3}0.011{col 58}{space 4}-1.185688{col 71}{space 3}-.1558792
{txt}{space 16} {c |}
{space 10}region {c |}
{space 8}Central  {c |}{col 18}{res}{space 2} .7260468{col 30}{space 2} .1185083{col 41}{space 1}    6.13{col 50}{space 3}0.000{col 58}{space 4} .4937748{col 71}{space 3} .9583187
{txt}{space 8}Eastern  {c |}{col 18}{res}{space 2} .7408855{col 30}{space 2} .1845728{col 41}{space 1}    4.01{col 50}{space 3}0.000{col 58}{space 4} .3791294{col 71}{space 3} 1.102642
{txt}{space 4}Rift Valley  {c |}{col 18}{res}{space 2} .5508552{col 30}{space 2} .1118653{col 41}{space 1}    4.92{col 50}{space 3}0.000{col 58}{space 4} .3316033{col 71}{space 3} .7701071
{txt}{space 9}Nyanza  {c |}{col 18}{res}{space 2} .8013812{col 30}{space 2} .1244368{col 41}{space 1}    6.44{col 50}{space 3}0.000{col 58}{space 4} .5574896{col 71}{space 3} 1.045273
{txt}{space 8}Western  {c |}{col 18}{res}{space 2}-.4680828{col 30}{space 2}  .134877{col 41}{space 1}   -3.47{col 50}{space 3}0.001{col 58}{space 4}-.7324369{col 71}{space 3}-.2037288
{txt}{space 2}North Eastern  {c |}{col 18}{res}{space 2} .6443786{col 30}{space 2} .3595896{col 41}{space 1}    1.79{col 50}{space 3}0.073{col 58}{space 4}-.0604041{col 71}{space 3} 1.349161
{txt}{space 10}Coast  {c |}{col 18}{res}{space 2}-.0413355{col 30}{space 2} .2206094{col 41}{space 1}   -0.19{col 50}{space 3}0.851{col 58}{space 4} -.473722{col 71}{space 3} .3910509
{txt}{space 16} {c |}
{space 7}Education {c |}{col 18}{res}{space 2}  .085789{col 30}{space 2} .0648062{col 41}{space 1}    1.32{col 50}{space 3}0.186{col 58}{space 4}-.0412288{col 71}{space 3} .2128069
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0013235{col 30}{space 2} .0047481{col 41}{space 1}    0.28{col 50}{space 3}0.780{col 58}{space 4}-.0079826{col 71}{space 3} .0106296
{txt}{space 11}Urban {c |}{col 18}{res}{space 2} .1323702{col 30}{space 2} .1182254{col 41}{space 1}    1.12{col 50}{space 3}0.263{col 58}{space 4}-.0993474{col 71}{space 3} .3640878
{txt}{space 10}Hunger {c |}{col 18}{res}{space 2}-.1266885{col 30}{space 2} .0952289{col 41}{space 1}   -1.33{col 50}{space 3}0.183{col 58}{space 4}-.3133337{col 71}{space 3} .0599566
{txt}{space 12}Male {c |}{col 18}{res}{space 2} .0907768{col 30}{space 2} .1732085{col 41}{space 1}    0.52{col 50}{space 3}0.600{col 58}{space 4}-.2487056{col 71}{space 3} .4302592
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-.0619963{col 30}{space 2} .8737922{col 58}{space 4}-1.774598{col 71}{space 3} 1.650605
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2} 1.872857{col 30}{space 2} .8420641{col 58}{space 4} .2224412{col 71}{space 3} 3.523272
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. preserve
{txt}
{com}.         margins, at(Musl=(0 1) trustgap=(-1(.25)1) ) post 
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       711
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._predict}:{space 1}{res:Pr(CollectiveA_2==0), predict(pr outcome(0))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._predict}:{space 1}{res:Pr(CollectiveA_2==1), predict(pr outcome(1))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._predict}:{space 1}{res:Pr(CollectiveA_2==2), predict(pr outcome(2))}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 9}-1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 9}-1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 7}-.75}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 7}-.75}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 8}-.5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 8}-.5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 7}-.25}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 7}-.25}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:9._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:10._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:11._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 8}.25}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:12._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 8}.25}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:13._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 9}.5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:14._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 9}.5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:15._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 8}.75}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:16._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 8}.75}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:17._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:18._at}:{space 1}{res:{txt:trustgap}{space 8}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:Musl}{space 12}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_predict#_at {c |}
{space 7}1  1  {c |}{col 14}{res}{space 2} .1680687{col 26}{space 2} .0459689{col 37}{space 1}    3.66{col 46}{space 3}0.000{col 54}{space 4} .0779714{col 67}{space 3}  .258166
{txt}{space 7}1  2  {c |}{col 14}{res}{space 2} .0132236{col 26}{space 2}  .015661{col 37}{space 1}    0.84{col 46}{space 3}0.398{col 54}{space 4}-.0174713{col 67}{space 3} .0439185
{txt}{space 7}1  3  {c |}{col 14}{res}{space 2} .1679564{col 26}{space 2} .0355113{col 37}{space 1}    4.73{col 46}{space 3}0.000{col 54}{space 4} .0983556{col 67}{space 3} .2375571
{txt}{space 7}1  4  {c |}{col 14}{res}{space 2} .0265169{col 26}{space 2} .0243492{col 37}{space 1}    1.09{col 46}{space 3}0.276{col 54}{space 4}-.0212067{col 67}{space 3} .0742405
{txt}{space 7}1  5  {c |}{col 14}{res}{space 2} .1678441{col 26}{space 2} .0254819{col 37}{space 1}    6.59{col 46}{space 3}0.000{col 54}{space 4} .1179004{col 67}{space 3} .2177877
{txt}{space 7}1  6  {c |}{col 14}{res}{space 2} .0520366{col 26}{space 2} .0343857{col 37}{space 1}    1.51{col 46}{space 3}0.130{col 54}{space 4}-.0153581{col 67}{space 3} .1194314
{txt}{space 7}1  7  {c |}{col 14}{res}{space 2} .1677318{col 26}{space 2} .0166508{col 37}{space 1}   10.07{col 46}{space 3}0.000{col 54}{space 4} .1350968{col 67}{space 3} .2003669
{txt}{space 7}1  8  {c |}{col 14}{res}{space 2}  .098444{col 26}{space 2} .0426797{col 37}{space 1}    2.31{col 46}{space 3}0.021{col 54}{space 4} .0147934{col 67}{space 3} .1820946
{txt}{space 7}1  9  {c |}{col 14}{res}{space 2} .1676196{col 26}{space 2} .0120038{col 37}{space 1}   13.96{col 46}{space 3}0.000{col 54}{space 4} .1440927{col 67}{space 3} .1911466
{txt}{space 7}1 10  {c |}{col 14}{res}{space 2} .1760898{col 26}{space 2}  .050095{col 37}{space 1}    3.52{col 46}{space 3}0.000{col 54}{space 4} .0779053{col 67}{space 3} .2742743
{txt}{space 7}1 11  {c |}{col 14}{res}{space 2} .1675075{col 26}{space 2} .0157837{col 37}{space 1}   10.61{col 46}{space 3}0.000{col 54}{space 4}  .136572{col 67}{space 3} .1984431
{txt}{space 7}1 12  {c |}{col 14}{res}{space 2} .2917881{col 26}{space 2} .0759731{col 37}{space 1}    3.84{col 46}{space 3}0.000{col 54}{space 4} .1428835{col 67}{space 3} .4406927
{txt}{space 7}1 13  {c |}{col 14}{res}{space 2} .1673955{col 26}{space 2} .0243236{col 37}{space 1}    6.88{col 46}{space 3}0.000{col 54}{space 4} .1197221{col 67}{space 3} .2150688
{txt}{space 7}1 14  {c |}{col 14}{res}{space 2} .4406364{col 26}{space 2} .1281867{col 37}{space 1}    3.44{col 46}{space 3}0.001{col 54}{space 4}  .189395{col 67}{space 3} .6918778
{txt}{space 7}1 15  {c |}{col 14}{res}{space 2} .1672835{col 26}{space 2} .0342185{col 37}{space 1}    4.89{col 46}{space 3}0.000{col 54}{space 4} .1002165{col 67}{space 3} .2343504
{txt}{space 7}1 16  {c |}{col 14}{res}{space 2}  .601781{col 26}{space 2} .1756431{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4}  .257527{col 67}{space 3} .9460351
{txt}{space 7}1 17  {c |}{col 14}{res}{space 2} .1671715{col 26}{space 2} .0445668{col 37}{space 1}    3.75{col 46}{space 3}0.000{col 54}{space 4} .0798221{col 67}{space 3} .2545209
{txt}{space 7}1 18  {c |}{col 14}{res}{space 2} .7461198{col 26}{space 2} .1856396{col 37}{space 1}    4.02{col 46}{space 3}0.000{col 54}{space 4} .3822728{col 67}{space 3} 1.109967
{txt}{space 7}2  1  {c |}{col 14}{res}{space 2} .3702213{col 26}{space 2} .0613828{col 37}{space 1}    6.03{col 46}{space 3}0.000{col 54}{space 4} .2499132{col 67}{space 3} .4905294
{txt}{space 7}2  2  {c |}{col 14}{res}{space 2} .0678902{col 26}{space 2} .0706847{col 37}{space 1}    0.96{col 46}{space 3}0.337{col 54}{space 4}-.0706493{col 67}{space 3} .2064296
{txt}{space 7}2  3  {c |}{col 14}{res}{space 2} .3701377{col 26}{space 2} .0538726{col 37}{space 1}    6.87{col 46}{space 3}0.000{col 54}{space 4} .2645494{col 67}{space 3}  .475726
{txt}{space 7}2  4  {c |}{col 14}{res}{space 2} .1214601{col 26}{space 2} .0882131{col 37}{space 1}    1.38{col 46}{space 3}0.169{col 54}{space 4}-.0514344{col 67}{space 3} .2943546
{txt}{space 7}2  5  {c |}{col 14}{res}{space 2}  .370054{col 26}{space 2} .0465538{col 37}{space 1}    7.95{col 46}{space 3}0.000{col 54}{space 4} .2788102{col 67}{space 3} .4612978
{txt}{space 7}2  6  {c |}{col 14}{res}{space 2} .1995971{col 26}{space 2}  .087994{col 37}{space 1}    2.27{col 46}{space 3}0.023{col 54}{space 4}  .027132{col 67}{space 3} .3720622
{txt}{space 7}2  7  {c |}{col 14}{res}{space 2} .3699703{col 26}{space 2} .0395408{col 37}{space 1}    9.36{col 46}{space 3}0.000{col 54}{space 4} .2924717{col 67}{space 3} .4474688
{txt}{space 7}2  8  {c |}{col 14}{res}{space 2} .2932185{col 26}{space 2} .0640554{col 37}{space 1}    4.58{col 46}{space 3}0.000{col 54}{space 4} .1676722{col 67}{space 3} .4187647
{txt}{space 7}2  9  {c |}{col 14}{res}{space 2} .3698865{col 26}{space 2} .0330382{col 37}{space 1}   11.20{col 46}{space 3}0.000{col 54}{space 4} .3051327{col 67}{space 3} .4346402
{txt}{space 7}2 10  {c |}{col 14}{res}{space 2} .3759205{col 26}{space 2}  .035414{col 37}{space 1}   10.62{col 46}{space 3}0.000{col 54}{space 4} .3065103{col 67}{space 3} .4453308
{txt}{space 7}2 11  {c |}{col 14}{res}{space 2} .3698026{col 26}{space 2} .0274229{col 37}{space 1}   13.49{col 46}{space 3}0.000{col 54}{space 4} .3160546{col 67}{space 3} .4235506
{txt}{space 7}2 12  {c |}{col 14}{res}{space 2} .4127302{col 26}{space 2} .0320649{col 37}{space 1}   12.87{col 46}{space 3}0.000{col 54}{space 4} .3498842{col 67}{space 3} .4755761
{txt}{space 7}2 13  {c |}{col 14}{res}{space 2} .3697187{col 26}{space 2} .0233575{col 37}{space 1}   15.83{col 46}{space 3}0.000{col 54}{space 4} .3239389{col 67}{space 3} .4154984
{txt}{space 7}2 14  {c |}{col 14}{res}{space 2} .3837101{col 26}{space 2}  .063345{col 37}{space 1}    6.06{col 46}{space 3}0.000{col 54}{space 4} .2595562{col 67}{space 3} .5078641
{txt}{space 7}2 15  {c |}{col 14}{res}{space 2} .3696347{col 26}{space 2} .0217439{col 37}{space 1}   17.00{col 46}{space 3}0.000{col 54}{space 4} .3270174{col 67}{space 3} .4122519
{txt}{space 7}2 16  {c |}{col 14}{res}{space 2} .3019513{col 26}{space 2} .1143852{col 37}{space 1}    2.64{col 46}{space 3}0.008{col 54}{space 4} .0777605{col 67}{space 3} .5261421
{txt}{space 7}2 17  {c |}{col 14}{res}{space 2} .3695506{col 26}{space 2} .0231152{col 37}{space 1}   15.99{col 46}{space 3}0.000{col 54}{space 4} .3242457{col 67}{space 3} .4148555
{txt}{space 7}2 18  {c |}{col 14}{res}{space 2} .2038748{col 26}{space 2} .1397625{col 37}{space 1}    1.46{col 46}{space 3}0.145{col 54}{space 4}-.0700546{col 67}{space 3} .4778043
{txt}{space 7}3  1  {c |}{col 14}{res}{space 2}   .46171{col 26}{space 2} .1024576{col 37}{space 1}    4.51{col 46}{space 3}0.000{col 54}{space 4} .2608968{col 67}{space 3} .6625232
{txt}{space 7}3  2  {c |}{col 14}{res}{space 2} .9188862{col 26}{space 2} .0861613{col 37}{space 1}   10.66{col 46}{space 3}0.000{col 54}{space 4} .7500132{col 67}{space 3} 1.087759
{txt}{space 7}3  3  {c |}{col 14}{res}{space 2} .4619059{col 26}{space 2} .0835524{col 37}{space 1}    5.53{col 46}{space 3}0.000{col 54}{space 4} .2981462{col 67}{space 3} .6256657
{txt}{space 7}3  4  {c |}{col 14}{res}{space 2}  .852023{col 26}{space 2} .1120939{col 37}{space 1}    7.60{col 46}{space 3}0.000{col 54}{space 4} .6323231{col 67}{space 3} 1.071723
{txt}{space 7}3  5  {c |}{col 14}{res}{space 2} .4621019{col 26}{space 2} .0647391{col 37}{space 1}    7.14{col 46}{space 3}0.000{col 54}{space 4} .3352155{col 67}{space 3} .5889883
{txt}{space 7}3  6  {c |}{col 14}{res}{space 2} .7483663{col 26}{space 2} .1210834{col 37}{space 1}    6.18{col 46}{space 3}0.000{col 54}{space 4} .5110472{col 67}{space 3} .9856854
{txt}{space 7}3  7  {c |}{col 14}{res}{space 2} .4622979{col 26}{space 2} .0461322{col 37}{space 1}   10.02{col 46}{space 3}0.000{col 54}{space 4} .3718804{col 67}{space 3} .5527154
{txt}{space 7}3  8  {c |}{col 14}{res}{space 2} .6083376{col 26}{space 2} .1026871{col 37}{space 1}    5.92{col 46}{space 3}0.000{col 54}{space 4} .4070745{col 67}{space 3} .8096006
{txt}{space 7}3  9  {c |}{col 14}{res}{space 2} .4624939{col 26}{space 2} .0281472{col 37}{space 1}   16.43{col 46}{space 3}0.000{col 54}{space 4} .4073263{col 67}{space 3} .5176614
{txt}{space 7}3 10  {c |}{col 14}{res}{space 2} .4479896{col 26}{space 2} .0714798{col 37}{space 1}    6.27{col 46}{space 3}0.000{col 54}{space 4} .3078918{col 67}{space 3} .5880875
{txt}{space 7}3 11  {c |}{col 14}{res}{space 2} .4626899{col 26}{space 2} .0135492{col 37}{space 1}   34.15{col 46}{space 3}0.000{col 54}{space 4} .4361339{col 67}{space 3} .4892459
{txt}{space 7}3 12  {c |}{col 14}{res}{space 2} .2954817{col 26}{space 2} .0656157{col 37}{space 1}    4.50{col 46}{space 3}0.000{col 54}{space 4} .1668774{col 67}{space 3} .4240861
{txt}{space 7}3 13  {c |}{col 14}{res}{space 2} .4628859{col 26}{space 2} .0173868{col 37}{space 1}   26.62{col 46}{space 3}0.000{col 54}{space 4} .4288083{col 67}{space 3} .4969635
{txt}{space 7}3 14  {c |}{col 14}{res}{space 2} .1756535{col 26}{space 2} .0716211{col 37}{space 1}    2.45{col 46}{space 3}0.014{col 54}{space 4} .0352787{col 67}{space 3} .3160283
{txt}{space 7}3 15  {c |}{col 14}{res}{space 2} .4630819{col 26}{space 2}  .033891{col 37}{space 1}   13.66{col 46}{space 3}0.000{col 54}{space 4} .3966568{col 67}{space 3}  .529507
{txt}{space 7}3 16  {c |}{col 14}{res}{space 2} .0962677{col 26}{space 2} .0634947{col 37}{space 1}    1.52{col 46}{space 3}0.129{col 54}{space 4}-.0281796{col 67}{space 3} .2207149
{txt}{space 7}3 17  {c |}{col 14}{res}{space 2} .4632779{col 26}{space 2} .0521789{col 37}{space 1}    8.88{col 46}{space 3}0.000{col 54}{space 4} .3610091{col 67}{space 3} .5655467
{txt}{space 7}3 18  {c |}{col 14}{res}{space 2} .0500054{col 26}{space 2}  .046712{col 37}{space 1}    1.07{col 46}{space 3}0.284{col 54}{space 4}-.0415484{col 67}{space 3} .1415593
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         parmest, label norestore 
{res}{txt}
{com}.         egen muslim = seq(), f(0) t(1) 
{txt}
{com}.                 lab def muslim 0 "Non-Muslims" 1 "Muslims"
{txt}
{com}.                 lab val muslim muslim
{txt}
{com}.         egen trustgap = seq(), f(0) t(8) block(2)
{txt}
{com}.                 replace trustgap = (trustgap/4)-1
{txt}variable {bf}trustgap{sf} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(54 real changes made)

{com}.         egen outcome = seq(), f(1) t(3) block(18)
{txt}
{com}.         graph twoway (rcap min95 max95 trustgap if outcome != 2, lcolor(gs10)) ///
>                          (connected estimate trustgap if outcome == 1, lcolor(gs5) mcolor(gs5) msymbol(D)) ///
>                          (connected estimate trustgap if outcome == 3, lcolor(gs10) lwidth(thick) lpattern(dash_dot) mcolor(gs10) msymbol(O)), ///
>                 by(muslim, graphregion(fcolor(white) lcolor(black)) note(" ")           )       ///
>                 ytitle("Predicted Agency Beliefs") xtitle("Gap in Trust")  xlabel(-1 "State > Religious" 0 "Equal Trust" 1 "Religious > State") xscale(r(-1.15 1.15)) ///
>                 legend(order(2 "Can do nothing" 3 "Can do a lot")) ylabel(0(.2)1) yscale(r(0 1)) xsize(6.5)
{res}{txt}
{com}. restore
{txt}
{com}.                 
. 
. **************************************************************************************************************************************************************************************
. ********************************************************************************** APPENDIX *******************************************************************************************
. 
. ************* Appendix Table 1: Who gets the question about environmental efficacy?
. global indvars Education Age Urban Hunger Male masai somali pastoralists i.region 
{txt}
{com}. global modelspecs [pweight= withinwt], cluster(region)
{txt}
{com}. 
. 
. ************ Appendix Table 2: More exploration of ethnicity
. tab Q84 Musl

          {txt}Q84. Ethnic {c |}   Q98 Muslim, Sunni
  community, cultural {c |}    only, Shia only
       group or tribe {c |}         0          1 {c |}     Total
{hline 22}{c +}{hline 22}{c +}{hline 10}
               Kikuyu {c |}{res}       293          2 {txt}{c |}{res}       295 
{txt}                  Luo {c |}{res}       179          0 {txt}{c |}{res}       179 
{txt}                Luhya {c |}{res}       215          5 {txt}{c |}{res}       220 
{txt}                Kamba {c |}{res}       186          1 {txt}{c |}{res}       187 
{txt}             Kalenjin {c |}{res}       158          0 {txt}{c |}{res}       158 
{txt}                Kisii {c |}{res}        96          0 {txt}{c |}{res}        96 
{txt}            Meru/Embu {c |}{res}       111          1 {txt}{c |}{res}       112 
{txt}        Masai/Samburu {c |}{res}        46          0 {txt}{c |}{res}        46 
{txt}            MijiKenda {c |}{res}        27         33 {txt}{c |}{res}        60 
{txt}                Taita {c |}{res}         9          1 {txt}{c |}{res}        10 
{txt}               Somali {c |}{res}         1         83 {txt}{c |}{res}        84 
{txt}                Pokot {c |}{res}        15          1 {txt}{c |}{res}        16 
{txt}              Turkana {c |}{res}        29          1 {txt}{c |}{res}        30 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
                Total {c |}{res}     1,365        128 {txt}{c |}{res}     1,493 
{txt}
{com}. global indvars pastoralists i.region Education Age Urban Hunger Male 
{txt}
{com}. global modelspecs [pweight= withinwt], cluster(region)
{txt}
{com}. eststo clear
{txt}
{com}. ologit CollectiveA_2 3.religion i.Q84 $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -732.3152}  
Iteration 1:{space 3}log pseudolikelihood = {res:-688.62124}  
Iteration 2:{space 3}log pseudolikelihood = {res:-687.95839}  
Iteration 3:{space 3}log pseudolikelihood = {res:-687.95719}  
Iteration 4:{space 3}log pseudolikelihood = {res:-687.95719}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       714
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-687.95719{txt}{col 49}Pseudo R2{col 67}= {res}    0.0606

{txt}{ralign 80:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1} CollectiveA_2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}religion {c |}
{space 7}Muslim  {c |}{col 16}{res}{space 2}-.7249687{col 28}{space 2}  .434914{col 39}{space 1}   -1.67{col 48}{space 3}0.096{col 56}{space 4}-1.577384{col 69}{space 3}  .127447
{txt}{space 14} {c |}
{space 11}Q84 {c |}
{space 10}Luo  {c |}{col 16}{res}{space 2}-.4510708{col 28}{space 2} .2054039{col 39}{space 1}   -2.20{col 48}{space 3}0.028{col 56}{space 4} -.853655{col 69}{space 3}-.0484866
{txt}{space 8}Luhya  {c |}{col 16}{res}{space 2}-.1667405{col 28}{space 2} .1664462{col 39}{space 1}   -1.00{col 48}{space 3}0.316{col 56}{space 4}-.4929691{col 69}{space 3} .1594881
{txt}{space 8}Kamba  {c |}{col 16}{res}{space 2}-.3015968{col 28}{space 2} .4145909{col 39}{space 1}   -0.73{col 48}{space 3}0.467{col 56}{space 4} -1.11418{col 69}{space 3} .5109864
{txt}{space 5}Kalenjin  {c |}{col 16}{res}{space 2} .0452368{col 28}{space 2} .1772909{col 39}{space 1}    0.26{col 48}{space 3}0.799{col 56}{space 4} -.302247{col 69}{space 3} .3927207
{txt}{space 8}Kisii  {c |}{col 16}{res}{space 2}-.6763219{col 28}{space 2} .2346144{col 39}{space 1}   -2.88{col 48}{space 3}0.004{col 56}{space 4}-1.136158{col 69}{space 3}-.2164861
{txt}{space 4}Meru/Embu  {c |}{col 16}{res}{space 2}-.4995503{col 28}{space 2} .4913877{col 39}{space 1}   -1.02{col 48}{space 3}0.309{col 56}{space 4}-1.462653{col 69}{space 3}  .463552
{txt}Masai/Samburu  {c |}{col 16}{res}{space 2}-.6837055{col 28}{space 2} .4190225{col 39}{space 1}   -1.63{col 48}{space 3}0.103{col 56}{space 4}-1.504975{col 69}{space 3} .1375636
{txt}{space 4}MijiKenda  {c |}{col 16}{res}{space 2}-1.019359{col 28}{space 2} .3860914{col 39}{space 1}   -2.64{col 48}{space 3}0.008{col 56}{space 4}-1.776084{col 69}{space 3}-.2626337
{txt}{space 8}Taita  {c |}{col 16}{res}{space 2}-1.202463{col 28}{space 2} .3831871{col 39}{space 1}   -3.14{col 48}{space 3}0.002{col 56}{space 4}-1.953496{col 69}{space 3}-.4514303
{txt}{space 7}Somali  {c |}{col 16}{res}{space 2}-.2105159{col 28}{space 2} .4900579{col 39}{space 1}   -0.43{col 48}{space 3}0.668{col 56}{space 4}-1.171012{col 69}{space 3}   .74998
{txt}{space 8}Pokot  {c |}{col 16}{res}{space 2}-.2756686{col 28}{space 2} .3286214{col 39}{space 1}   -0.84{col 48}{space 3}0.402{col 56}{space 4}-.9197547{col 69}{space 3} .3684175
{txt}{space 6}Turkana  {c |}{col 16}{res}{space 2}-.0993655{col 28}{space 2} .7586867{col 39}{space 1}   -0.13{col 48}{space 3}0.896{col 56}{space 4}-1.586364{col 69}{space 3} 1.387633
{txt}{space 14} {c |}
{space 2}pastoralists {c |}{col 16}{res}{space 2}-.5792817{col 28}{space 2}   .32226{col 39}{space 1}   -1.80{col 48}{space 3}0.072{col 56}{space 4}  -1.2109{col 69}{space 3} .0523364
{txt}{space 14} {c |}
{space 8}region {c |}
{space 6}Central  {c |}{col 16}{res}{space 2} .3407107{col 28}{space 2} .0685759{col 39}{space 1}    4.97{col 48}{space 3}0.000{col 56}{space 4} .2063044{col 69}{space 3} .4751169
{txt}{space 6}Eastern  {c |}{col 16}{res}{space 2} .6222035{col 28}{space 2} .3338196{col 39}{space 1}    1.86{col 48}{space 3}0.062{col 56}{space 4}-.0320708{col 69}{space 3} 1.276478
{txt}{space 2}Rift Valley  {c |}{col 16}{res}{space 2} .4120827{col 28}{space 2} .0767554{col 39}{space 1}    5.37{col 48}{space 3}0.000{col 56}{space 4}  .261645{col 69}{space 3} .5625205
{txt}{space 7}Nyanza  {c |}{col 16}{res}{space 2} .9398202{col 28}{space 2} .1517311{col 39}{space 1}    6.19{col 48}{space 3}0.000{col 56}{space 4} .6424326{col 69}{space 3} 1.237208
{txt}{space 6}Western  {c |}{col 16}{res}{space 2}-.5610019{col 28}{space 2} .1520823{col 39}{space 1}   -3.69{col 48}{space 3}0.000{col 56}{space 4}-.8590776{col 69}{space 3}-.2629261
{txt}North Eastern  {c |}{col 16}{res}{space 2}-.4140905{col 28}{space 2} .2283862{col 39}{space 1}   -1.81{col 48}{space 3}0.070{col 56}{space 4}-.8617191{col 69}{space 3} .0335382
{txt}{space 8}Coast  {c |}{col 16}{res}{space 2}-.0653939{col 28}{space 2} .1259131{col 39}{space 1}   -0.52{col 48}{space 3}0.604{col 56}{space 4}-.3121791{col 69}{space 3} .1813913
{txt}{space 14} {c |}
{space 5}Education {c |}{col 16}{res}{space 2} .1070241{col 28}{space 2} .0714785{col 39}{space 1}    1.50{col 48}{space 3}0.134{col 56}{space 4}-.0330712{col 69}{space 3} .2471193
{txt}{space 11}Age {c |}{col 16}{res}{space 2}  .000593{col 28}{space 2} .0052101{col 39}{space 1}    0.11{col 48}{space 3}0.909{col 56}{space 4}-.0096186{col 69}{space 3} .0108046
{txt}{space 9}Urban {c |}{col 16}{res}{space 2} .0889131{col 28}{space 2} .1467177{col 39}{space 1}    0.61{col 48}{space 3}0.545{col 56}{space 4}-.1986483{col 69}{space 3} .3764746
{txt}{space 8}Hunger {c |}{col 16}{res}{space 2}-.1020465{col 28}{space 2} .1079691{col 39}{space 1}   -0.95{col 48}{space 3}0.345{col 56}{space 4} -.313662{col 69}{space 3}  .109569
{txt}{space 10}Male {c |}{col 16}{res}{space 2} .0813337{col 28}{space 2} .1752958{col 39}{space 1}    0.46{col 48}{space 3}0.643{col 56}{space 4}-.2622397{col 69}{space 3} .4249071
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2} -1.20607{col 28}{space 2} .5359048{col 56}{space 4}-2.256424{col 69}{space 3}-.1557159
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2} .7034116{col 28}{space 2} .4743618{col 56}{space 4}-.2263204{col 69}{space 3} 1.633144
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est1{txt} stored)

{com}. ologit CollectiveA_2 3.religion ethnicgrievance $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-763.18606}  
Iteration 1:{space 3}log pseudolikelihood = {res:-722.76893}  
Iteration 2:{space 3}log pseudolikelihood = {res:-722.29039}  
Iteration 3:{space 3}log pseudolikelihood = {res:-722.28977}  
Iteration 4:{space 3}log pseudolikelihood = {res:-722.28977}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       743
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-722.28977{txt}{col 49}Pseudo R2{col 67}= {res}    0.0536

{txt}{ralign 81:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  CollectiveA_2{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}religion {c |}
{space 8}Muslim  {c |}{col 17}{res}{space 2}-.8498175{col 29}{space 2} .3952296{col 40}{space 1}   -2.15{col 49}{space 3}0.032{col 57}{space 4}-1.624453{col 70}{space 3}-.0751817
{txt}ethnicgrievance {c |}{col 17}{res}{space 2}-.0111827{col 29}{space 2} .2769406{col 40}{space 1}   -0.04{col 49}{space 3}0.968{col 57}{space 4}-.5539762{col 70}{space 3} .5316108
{txt}{space 3}pastoralists {c |}{col 17}{res}{space 2}-.8349236{col 29}{space 2} .2377002{col 40}{space 1}   -3.51{col 49}{space 3}0.000{col 57}{space 4}-1.300807{col 70}{space 3}-.3690397
{txt}{space 15} {c |}
{space 9}region {c |}
{space 7}Central  {c |}{col 17}{res}{space 2} .4539034{col 29}{space 2} .0680896{col 40}{space 1}    6.67{col 49}{space 3}0.000{col 57}{space 4} .3204501{col 70}{space 3} .5873566
{txt}{space 7}Eastern  {c |}{col 17}{res}{space 2}  .376524{col 29}{space 2} .1008273{col 40}{space 1}    3.73{col 49}{space 3}0.000{col 57}{space 4} .1789062{col 70}{space 3} .5741418
{txt}{space 3}Rift Valley  {c |}{col 17}{res}{space 2} .4730165{col 29}{space 2} .0929989{col 40}{space 1}    5.09{col 49}{space 3}0.000{col 57}{space 4} .2907421{col 70}{space 3}  .655291
{txt}{space 8}Nyanza  {c |}{col 17}{res}{space 2} .6178901{col 29}{space 2} .1142442{col 40}{space 1}    5.41{col 49}{space 3}0.000{col 57}{space 4} .3939756{col 70}{space 3} .8418045
{txt}{space 7}Western  {c |}{col 17}{res}{space 2}-.4325317{col 29}{space 2} .1368849{col 40}{space 1}   -3.16{col 49}{space 3}0.002{col 57}{space 4}-.7008212{col 70}{space 3}-.1642422
{txt}{space 1}North Eastern  {c |}{col 17}{res}{space 2}-.0516294{col 29}{space 2} .4420007{col 40}{space 1}   -0.12{col 49}{space 3}0.907{col 57}{space 4}-.9179349{col 70}{space 3}  .814676
{txt}{space 9}Coast  {c |}{col 17}{res}{space 2}-.4066079{col 29}{space 2} .1746527{col 40}{space 1}   -2.33{col 49}{space 3}0.020{col 57}{space 4}-.7489208{col 70}{space 3} -.064295
{txt}{space 15} {c |}
{space 6}Education {c |}{col 17}{res}{space 2}  .126231{col 29}{space 2} .0743306{col 40}{space 1}    1.70{col 49}{space 3}0.089{col 57}{space 4}-.0194543{col 70}{space 3} .2719163
{txt}{space 12}Age {c |}{col 17}{res}{space 2} .0022748{col 29}{space 2} .0058442{col 40}{space 1}    0.39{col 49}{space 3}0.697{col 57}{space 4}-.0091795{col 70}{space 3} .0137292
{txt}{space 10}Urban {c |}{col 17}{res}{space 2} .0901273{col 29}{space 2} .1249902{col 40}{space 1}    0.72{col 49}{space 3}0.471{col 57}{space 4} -.154849{col 70}{space 3} .3351036
{txt}{space 9}Hunger {c |}{col 17}{res}{space 2}-.1390173{col 29}{space 2} .0994917{col 40}{space 1}   -1.40{col 49}{space 3}0.162{col 57}{space 4}-.3340174{col 70}{space 3} .0559828
{txt}{space 11}Male {c |}{col 17}{res}{space 2} .0191525{col 29}{space 2} .1658594{col 40}{space 1}    0.12{col 49}{space 3}0.908{col 57}{space 4} -.305926{col 70}{space 3}  .344231
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2}-.8530114{col 29}{space 2} .5799086{col 57}{space 4}-1.989611{col 70}{space 3} .2835886
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2} .9796357{col 29}{space 2} .5450078{col 57}{space 4}  -.08856{col 70}{space 3} 2.047831
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est2{txt} stored)

{com}. ologit CollectiveA_2 3.religion kenyan_v_ethnicID $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-762.02955}  
Iteration 1:{space 3}log pseudolikelihood = {res:-721.03389}  
Iteration 2:{space 3}log pseudolikelihood = {res:-720.54761}  
Iteration 3:{space 3}log pseudolikelihood = {res:-720.54696}  
Iteration 4:{space 3}log pseudolikelihood = {res:-720.54696}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       742
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-720.54696{txt}{col 49}Pseudo R2{col 67}= {res}    0.0544

{txt}{ralign 83:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}    CollectiveA_2{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}religion {c |}
{space 10}Muslim  {c |}{col 19}{res}{space 2}-.8671031{col 31}{space 2}  .390059{col 42}{space 1}   -2.22{col 51}{space 3}0.026{col 59}{space 4}-1.631605{col 72}{space 3}-.1026015
{txt}kenyan_v_ethnicID {c |}{col 19}{res}{space 2} -.273323{col 31}{space 2} .4240319{col 42}{space 1}   -0.64{col 51}{space 3}0.519{col 59}{space 4} -1.10441{col 72}{space 3} .5577642
{txt}{space 5}pastoralists {c |}{col 19}{res}{space 2}-.8081152{col 31}{space 2} .2621302{col 42}{space 1}   -3.08{col 51}{space 3}0.002{col 59}{space 4}-1.321881{col 72}{space 3}-.2943494
{txt}{space 17} {c |}
{space 11}region {c |}
{space 9}Central  {c |}{col 19}{res}{space 2} .4552304{col 31}{space 2} .0630682{col 42}{space 1}    7.22{col 51}{space 3}0.000{col 59}{space 4}  .331619{col 72}{space 3} .5788417
{txt}{space 9}Eastern  {c |}{col 19}{res}{space 2} .3921728{col 31}{space 2} .1120772{col 42}{space 1}    3.50{col 51}{space 3}0.000{col 59}{space 4} .1725056{col 72}{space 3}   .61184
{txt}{space 5}Rift Valley  {c |}{col 19}{res}{space 2}  .489084{col 31}{space 2} .1074827{col 42}{space 1}    4.55{col 51}{space 3}0.000{col 59}{space 4} .2784218{col 72}{space 3} .6997463
{txt}{space 10}Nyanza  {c |}{col 19}{res}{space 2} .6462258{col 31}{space 2} .1141893{col 42}{space 1}    5.66{col 51}{space 3}0.000{col 59}{space 4} .4224188{col 72}{space 3} .8700329
{txt}{space 9}Western  {c |}{col 19}{res}{space 2}-.4034491{col 31}{space 2} .1526975{col 42}{space 1}   -2.64{col 51}{space 3}0.008{col 59}{space 4}-.7027307{col 72}{space 3}-.1041675
{txt}{space 3}North Eastern  {c |}{col 19}{res}{space 2}-.0041333{col 31}{space 2}  .431196{col 42}{space 1}   -0.01{col 51}{space 3}0.992{col 59}{space 4}-.8492618{col 72}{space 3} .8409953
{txt}{space 11}Coast  {c |}{col 19}{res}{space 2}-.3689935{col 31}{space 2} .1864344{col 42}{space 1}   -1.98{col 51}{space 3}0.048{col 59}{space 4}-.7343983{col 72}{space 3}-.0035888
{txt}{space 17} {c |}
{space 8}Education {c |}{col 19}{res}{space 2} .1207101{col 31}{space 2} .0738241{col 42}{space 1}    1.64{col 51}{space 3}0.102{col 59}{space 4}-.0239826{col 72}{space 3} .2654028
{txt}{space 14}Age {c |}{col 19}{res}{space 2} .0027121{col 31}{space 2} .0060963{col 42}{space 1}    0.44{col 51}{space 3}0.656{col 59}{space 4}-.0092363{col 72}{space 3} .0146606
{txt}{space 12}Urban {c |}{col 19}{res}{space 2} .1083251{col 31}{space 2} .1374703{col 42}{space 1}    0.79{col 51}{space 3}0.431{col 59}{space 4}-.1611118{col 72}{space 3} .3777621
{txt}{space 11}Hunger {c |}{col 19}{res}{space 2}-.1464661{col 31}{space 2}  .093349{col 42}{space 1}   -1.57{col 51}{space 3}0.117{col 59}{space 4}-.3294268{col 72}{space 3} .0364947
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0301363{col 31}{space 2} .1539735{col 42}{space 1}    0.20{col 51}{space 3}0.845{col 59}{space 4}-.2716463{col 72}{space 3} .3319188
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-1.026196{col 31}{space 2} .6159186{col 59}{space 4}-2.233375{col 72}{space 3} .1809819
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2} .8036833{col 31}{space 2} .5435571{col 59}{space 4} -.261669{col 72}{space 3} 1.869036
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est3{txt} stored)

{com}. esttab using AppTable_eff, tex replace b(3) se(3) label star(* .10 ** .05 *** .01) nogaps nomtitles nodepvars
{res}{txt}(note: file AppTable_eff.tex not found)
(output written to {browse  `"AppTable_eff.tex"'})

{com}.         
. 
. 
. ************ Appendix Table 3: Issue Salience (with Controls) ****************************************************************
. global indvars 5.religiousID Education Age Urban Hunger Male masai somali pastoralists i.region 
{txt}
{com}. global modelspecs [pweight= withinwt], cluster(region)
{txt}
{com}. eststo clear
{txt}
{com}. ologit climateperception $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1426.1797}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1365.1394}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1363.8633}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1363.8584}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1363.8584}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,442
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-1363.8584{txt}{col 49}Pseudo R2{col 67}= {res}    0.0437

{txt}{ralign 83:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}climateperception{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}religiousID {c |}
{space 10}Muslim  {c |}{col 19}{res}{space 2}-.0633606{col 31}{space 2} .2821126{col 42}{space 1}   -0.22{col 51}{space 3}0.822{col 59}{space 4}-.6162911{col 72}{space 3} .4895698
{txt}{space 8}Education {c |}{col 19}{res}{space 2} -.015073{col 31}{space 2} .0445727{col 42}{space 1}   -0.34{col 51}{space 3}0.735{col 59}{space 4}-.1024338{col 72}{space 3} .0722878
{txt}{space 14}Age {c |}{col 19}{res}{space 2} .0124952{col 31}{space 2} .0067098{col 42}{space 1}    1.86{col 51}{space 3}0.063{col 59}{space 4}-.0006558{col 72}{space 3} .0256461
{txt}{space 12}Urban {c |}{col 19}{res}{space 2} .0769071{col 31}{space 2}  .199626{col 42}{space 1}    0.39{col 51}{space 3}0.700{col 59}{space 4}-.3143525{col 72}{space 3} .4681668
{txt}{space 11}Hunger {c |}{col 19}{res}{space 2} .1054044{col 31}{space 2}  .067377{col 42}{space 1}    1.56{col 51}{space 3}0.118{col 59}{space 4}-.0266522{col 72}{space 3}  .237461
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .1838537{col 31}{space 2} .1497806{col 42}{space 1}    1.23{col 51}{space 3}0.220{col 59}{space 4} -.109711{col 72}{space 3} .4774183
{txt}{space 12}masai {c |}{col 19}{res}{space 2} .6979335{col 31}{space 2} .5956987{col 42}{space 1}    1.17{col 51}{space 3}0.241{col 59}{space 4}-.4696146{col 72}{space 3} 1.865482
{txt}{space 11}somali {c |}{col 19}{res}{space 2}-.6793534{col 31}{space 2} .4582911{col 42}{space 1}   -1.48{col 51}{space 3}0.138{col 59}{space 4}-1.577587{col 72}{space 3} .2188806
{txt}{space 5}pastoralists {c |}{col 19}{res}{space 2} .1525272{col 31}{space 2} .6658194{col 42}{space 1}    0.23{col 51}{space 3}0.819{col 59}{space 4}-1.152455{col 72}{space 3} 1.457509
{txt}{space 17} {c |}
{space 11}region {c |}
{space 9}Central  {c |}{col 19}{res}{space 2} .0890608{col 31}{space 2} .1250986{col 42}{space 1}    0.71{col 51}{space 3}0.477{col 59}{space 4}-.1561278{col 72}{space 3} .3342495
{txt}{space 9}Eastern  {c |}{col 19}{res}{space 2} .2347638{col 31}{space 2} .1583981{col 42}{space 1}    1.48{col 51}{space 3}0.138{col 59}{space 4}-.0756907{col 72}{space 3} .5452183
{txt}{space 5}Rift Valley  {c |}{col 19}{res}{space 2}-.6937344{col 31}{space 2} .1476381{col 42}{space 1}   -4.70{col 51}{space 3}0.000{col 59}{space 4}-.9830998{col 72}{space 3}-.4043691
{txt}{space 10}Nyanza  {c |}{col 19}{res}{space 2} .3314297{col 31}{space 2} .1576744{col 42}{space 1}    2.10{col 51}{space 3}0.036{col 59}{space 4} .0223935{col 72}{space 3} .6404658
{txt}{space 9}Western  {c |}{col 19}{res}{space 2}-.2632534{col 31}{space 2} .1674658{col 42}{space 1}   -1.57{col 51}{space 3}0.116{col 59}{space 4}-.5914802{col 72}{space 3} .0649735
{txt}{space 3}North Eastern  {c |}{col 19}{res}{space 2} 2.306054{col 31}{space 2} .8560574{col 42}{space 1}    2.69{col 51}{space 3}0.007{col 59}{space 4} .6282119{col 72}{space 3} 3.983895
{txt}{space 11}Coast  {c |}{col 19}{res}{space 2} .7474548{col 31}{space 2} .0946479{col 42}{space 1}    7.90{col 51}{space 3}0.000{col 59}{space 4} .5619484{col 72}{space 3} .9329612
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-.8100907{col 31}{space 2} .4731009{col 59}{space 4}-1.737351{col 72}{space 3}   .11717
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2} .3994563{col 31}{space 2}  .495785{col 59}{space 4}-.5722644{col 72}{space 3} 1.371177
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est1{txt} stored)

{com}. logit mip_1st $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-654.51487}  
Iteration 1:{space 3}log pseudolikelihood = {res:-563.13046}  
Iteration 2:{space 3}log pseudolikelihood = {res:-544.25955}  
Iteration 3:{space 3}log pseudolikelihood = {res:-543.60422}  
Iteration 4:{space 3}log pseudolikelihood = {res:-543.59916}  
Iteration 5:{space 3}log pseudolikelihood = {res:-543.59916}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,565
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(6)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-543.59916{txt}{col 49}Pseudo R2{col 67}= {res}    0.1695

{txt}{ralign 80:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}       mip_1st{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}religiousID {c |}
{space 7}Muslim  {c |}{col 16}{res}{space 2} .0529173{col 28}{space 2} .4075969{col 39}{space 1}    0.13{col 48}{space 3}0.897{col 56}{space 4}-.7459579{col 69}{space 3} .8517925
{txt}{space 5}Education {c |}{col 16}{res}{space 2}-.1827514{col 28}{space 2} .0377082{col 39}{space 1}   -4.85{col 48}{space 3}0.000{col 56}{space 4}-.2566581{col 69}{space 3}-.1088447
{txt}{space 11}Age {c |}{col 16}{res}{space 2} .0050859{col 28}{space 2} .0063772{col 39}{space 1}    0.80{col 48}{space 3}0.425{col 56}{space 4}-.0074132{col 69}{space 3}  .017585
{txt}{space 9}Urban {c |}{col 16}{res}{space 2}-.4678489{col 28}{space 2} .2881009{col 39}{space 1}   -1.62{col 48}{space 3}0.104{col 56}{space 4}-1.032516{col 69}{space 3} .0968185
{txt}{space 8}Hunger {c |}{col 16}{res}{space 2} .1173692{col 28}{space 2} .0512727{col 39}{space 1}    2.29{col 48}{space 3}0.022{col 56}{space 4} .0168766{col 69}{space 3} .2178618
{txt}{space 10}Male {c |}{col 16}{res}{space 2} .0664714{col 28}{space 2} .2202705{col 39}{space 1}    0.30{col 48}{space 3}0.763{col 56}{space 4} -.365251{col 69}{space 3} .4981937
{txt}{space 9}masai {c |}{col 16}{res}{space 2} .0563474{col 28}{space 2} .2100546{col 39}{space 1}    0.27{col 48}{space 3}0.789{col 56}{space 4}-.3553521{col 69}{space 3} .4680468
{txt}{space 8}somali {c |}{col 16}{res}{space 2}-.1057337{col 28}{space 2}  .900218{col 39}{space 1}   -0.12{col 48}{space 3}0.907{col 56}{space 4}-1.870128{col 69}{space 3} 1.658661
{txt}{space 2}pastoralists {c |}{col 16}{res}{space 2} 1.193109{col 28}{space 2} .3447619{col 39}{space 1}    3.46{col 48}{space 3}0.001{col 56}{space 4} .5173881{col 69}{space 3}  1.86883
{txt}{space 14} {c |}
{space 8}region {c |}
{space 6}Central  {c |}{col 16}{res}{space 2}-.0851879{col 28}{space 2} .1907236{col 39}{space 1}   -0.45{col 48}{space 3}0.655{col 56}{space 4}-.4589993{col 69}{space 3} .2886235
{txt}{space 6}Eastern  {c |}{col 16}{res}{space 2} 1.625886{col 28}{space 2} .2405205{col 39}{space 1}    6.76{col 48}{space 3}0.000{col 56}{space 4} 1.154474{col 69}{space 3} 2.097297
{txt}{space 2}Rift Valley  {c |}{col 16}{res}{space 2} .1814366{col 28}{space 2} .2250207{col 39}{space 1}    0.81{col 48}{space 3}0.420{col 56}{space 4}-.2595958{col 69}{space 3}  .622469
{txt}{space 7}Nyanza  {c |}{col 16}{res}{space 2} .3180623{col 28}{space 2} .2537616{col 39}{space 1}    1.25{col 48}{space 3}0.210{col 56}{space 4}-.1793012{col 69}{space 3} .8154259
{txt}{space 6}Western  {c |}{col 16}{res}{space 2}-1.448635{col 28}{space 2} .2764148{col 39}{space 1}   -5.24{col 48}{space 3}0.000{col 56}{space 4}-1.990398{col 69}{space 3}-.9068719
{txt}North Eastern  {c |}{col 16}{res}{space 2} 1.124002{col 28}{space 2} .6700968{col 39}{space 1}    1.68{col 48}{space 3}0.093{col 56}{space 4}-.1893634{col 69}{space 3} 2.437368
{txt}{space 8}Coast  {c |}{col 16}{res}{space 2} 1.443003{col 28}{space 2} .1549063{col 39}{space 1}    9.32{col 48}{space 3}0.000{col 56}{space 4} 1.139392{col 69}{space 3} 1.746614
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-2.067443{col 28}{space 2} .6035941{col 39}{space 1}   -3.43{col 48}{space 3}0.001{col 56}{space 4}-3.250466{col 69}{space 3}-.8844203
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est2{txt} stored)

{com}. logit mip_any $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1036.4282}  
Iteration 1:{space 3}log pseudolikelihood = {res:-915.09512}  
Iteration 2:{space 3}log pseudolikelihood = {res:-914.21005}  
Iteration 3:{space 3}log pseudolikelihood = {res:-914.20941}  
Iteration 4:{space 3}log pseudolikelihood = {res:-914.20941}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,565
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(6)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-914.20941{txt}{col 49}Pseudo R2{col 67}= {res}    0.1179

{txt}{ralign 80:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}       mip_any{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}religiousID {c |}
{space 7}Muslim  {c |}{col 16}{res}{space 2} .1011665{col 28}{space 2}  .391944{col 39}{space 1}    0.26{col 48}{space 3}0.796{col 56}{space 4}-.6670296{col 69}{space 3} .8693626
{txt}{space 5}Education {c |}{col 16}{res}{space 2}-.1091497{col 28}{space 2} .0292628{col 39}{space 1}   -3.73{col 48}{space 3}0.000{col 56}{space 4}-.1665038{col 69}{space 3}-.0517957
{txt}{space 11}Age {c |}{col 16}{res}{space 2} .0045967{col 28}{space 2} .0050974{col 39}{space 1}    0.90{col 48}{space 3}0.367{col 56}{space 4} -.005394{col 69}{space 3} .0145873
{txt}{space 9}Urban {c |}{col 16}{res}{space 2}-.3908395{col 28}{space 2} .2040978{col 39}{space 1}   -1.91{col 48}{space 3}0.055{col 56}{space 4}-.7908638{col 69}{space 3} .0091849
{txt}{space 8}Hunger {c |}{col 16}{res}{space 2} .1531323{col 28}{space 2} .0417654{col 39}{space 1}    3.67{col 48}{space 3}0.000{col 56}{space 4} .0712736{col 69}{space 3} .2349911
{txt}{space 10}Male {c |}{col 16}{res}{space 2} .0313405{col 28}{space 2}  .073288{col 39}{space 1}    0.43{col 48}{space 3}0.669{col 56}{space 4}-.1123013{col 69}{space 3} .1749823
{txt}{space 9}masai {c |}{col 16}{res}{space 2}-.3120097{col 28}{space 2} .5016827{col 39}{space 1}   -0.62{col 48}{space 3}0.534{col 56}{space 4} -1.29529{col 69}{space 3} .6712704
{txt}{space 8}somali {c |}{col 16}{res}{space 2} -.003979{col 28}{space 2} .5360926{col 39}{space 1}   -0.01{col 48}{space 3}0.994{col 56}{space 4}-1.054701{col 69}{space 3} 1.046743
{txt}{space 2}pastoralists {c |}{col 16}{res}{space 2} 1.289923{col 28}{space 2} .4670553{col 39}{space 1}    2.76{col 48}{space 3}0.006{col 56}{space 4} .3745117{col 69}{space 3} 2.205335
{txt}{space 14} {c |}
{space 8}region {c |}
{space 6}Central  {c |}{col 16}{res}{space 2} .2607783{col 28}{space 2} .1460729{col 39}{space 1}    1.79{col 48}{space 3}0.074{col 56}{space 4}-.0255193{col 69}{space 3} .5470759
{txt}{space 6}Eastern  {c |}{col 16}{res}{space 2} 1.218472{col 28}{space 2} .1552135{col 39}{space 1}    7.85{col 48}{space 3}0.000{col 56}{space 4} .9142593{col 69}{space 3} 1.522685
{txt}{space 2}Rift Valley  {c |}{col 16}{res}{space 2} .3675212{col 28}{space 2}  .149501{col 39}{space 1}    2.46{col 48}{space 3}0.014{col 56}{space 4} .0745047{col 69}{space 3} .6605377
{txt}{space 7}Nyanza  {c |}{col 16}{res}{space 2} .3804484{col 28}{space 2} .1625674{col 39}{space 1}    2.34{col 48}{space 3}0.019{col 56}{space 4} .0618222{col 69}{space 3} .6990746
{txt}{space 6}Western  {c |}{col 16}{res}{space 2}-.7226209{col 28}{space 2} .1728523{col 39}{space 1}   -4.18{col 48}{space 3}0.000{col 56}{space 4}-1.061405{col 69}{space 3}-.3838366
{txt}North Eastern  {c |}{col 16}{res}{space 2} 1.584225{col 28}{space 2} .4030812{col 39}{space 1}    3.93{col 48}{space 3}0.000{col 56}{space 4} .7942007{col 69}{space 3}  2.37425
{txt}{space 8}Coast  {c |}{col 16}{res}{space 2} .7848401{col 28}{space 2} .0815225{col 39}{space 1}    9.63{col 48}{space 3}0.000{col 56}{space 4}  .625059{col 69}{space 3} .9446212
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-.8096227{col 28}{space 2} .4147965{col 39}{space 1}   -1.95{col 48}{space 3}0.051{col 56}{space 4}-1.622609{col 69}{space 3} .0033636
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est3{txt} stored)

{com}. esttab using AppTable_salience, tex replace b(3) se(3) label star(* .10 ** .05 *** .01) nogaps nomtitles nodepvars
{res}{txt}(note: file AppTable_salience.tex not found)
(output written to {browse  `"AppTable_salience.tex"'})

{com}. 
. 
. *********** Appendix Table 4: What causes climate change? *************************************************************
. global indvars i.religion Education Age Urban Hunger Male masai somali pastoralists i.region 
{txt}
{com}. global modelspecs [pweight= withinwt], cluster(region)
{txt}
{com}. eststo clear
{txt}
{com}. logit HumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-650.71294}  
Iteration 1:{space 3}log pseudolikelihood = {res:-620.27607}  
Iteration 2:{space 3}log pseudolikelihood = {res:-620.09556}  
Iteration 3:{space 3}log pseudolikelihood = {res:-620.09538}  
Iteration 4:{space 3}log pseudolikelihood = {res:-620.09538}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       977
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(6)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-620.09538{txt}{col 49}Pseudo R2{col 67}= {res}    0.0471

{txt}{ralign 80:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        HumanA{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}religion {c |}
{space 4}Christian  {c |}{col 16}{res}{space 2}-.4876203{col 28}{space 2} .5772804{col 39}{space 1}   -0.84{col 48}{space 3}0.398{col 56}{space 4}-1.619069{col 69}{space 3} .6438285
{txt}{space 7}Muslim  {c |}{col 16}{res}{space 2}-.7112449{col 28}{space 2} .6841731{col 39}{space 1}   -1.04{col 48}{space 3}0.299{col 56}{space 4}-2.052199{col 69}{space 3} .6297097
{txt}{space 14} {c |}
{space 5}Education {c |}{col 16}{res}{space 2} .1036269{col 28}{space 2} .0721847{col 39}{space 1}    1.44{col 48}{space 3}0.151{col 56}{space 4}-.0378525{col 69}{space 3} .2451064
{txt}{space 11}Age {c |}{col 16}{res}{space 2} .0060195{col 28}{space 2} .0055659{col 39}{space 1}    1.08{col 48}{space 3}0.279{col 56}{space 4}-.0048894{col 69}{space 3} .0169283
{txt}{space 9}Urban {c |}{col 16}{res}{space 2} .0495464{col 28}{space 2} .1494854{col 39}{space 1}    0.33{col 48}{space 3}0.740{col 56}{space 4}-.2434396{col 69}{space 3} .3425324
{txt}{space 8}Hunger {c |}{col 16}{res}{space 2}-.0222826{col 28}{space 2} .0887055{col 39}{space 1}   -0.25{col 48}{space 3}0.802{col 56}{space 4}-.1961422{col 69}{space 3}  .151577
{txt}{space 10}Male {c |}{col 16}{res}{space 2}-.0540417{col 28}{space 2} .1357865{col 39}{space 1}   -0.40{col 48}{space 3}0.691{col 56}{space 4}-.3201783{col 69}{space 3} .2120949
{txt}{space 9}masai {c |}{col 16}{res}{space 2} 1.060175{col 28}{space 2} .6721981{col 39}{space 1}    1.58{col 48}{space 3}0.115{col 56}{space 4} -.257309{col 69}{space 3} 2.377659
{txt}{space 8}somali {c |}{col 16}{res}{space 2}-.7889216{col 28}{space 2} .6783686{col 39}{space 1}   -1.16{col 48}{space 3}0.245{col 56}{space 4}  -2.1185{col 69}{space 3} .5406565
{txt}{space 2}pastoralists {c |}{col 16}{res}{space 2}-.2809736{col 28}{space 2} .2283536{col 39}{space 1}   -1.23{col 48}{space 3}0.219{col 56}{space 4}-.7285385{col 69}{space 3} .1665913
{txt}{space 14} {c |}
{space 8}region {c |}
{space 6}Central  {c |}{col 16}{res}{space 2} .0189183{col 28}{space 2} .0823407{col 39}{space 1}    0.23{col 48}{space 3}0.818{col 56}{space 4}-.1424665{col 69}{space 3} .1803031
{txt}{space 6}Eastern  {c |}{col 16}{res}{space 2}-.6829124{col 28}{space 2} .0898837{col 39}{space 1}   -7.60{col 48}{space 3}0.000{col 56}{space 4}-.8590813{col 69}{space 3}-.5067435
{txt}{space 2}Rift Valley  {c |}{col 16}{res}{space 2} .2191931{col 28}{space 2} .1006269{col 39}{space 1}    2.18{col 48}{space 3}0.029{col 56}{space 4}  .021968{col 69}{space 3} .4164182
{txt}{space 7}Nyanza  {c |}{col 16}{res}{space 2}-.4430733{col 28}{space 2}  .083187{col 39}{space 1}   -5.33{col 48}{space 3}0.000{col 56}{space 4}-.6061168{col 69}{space 3}-.2800297
{txt}{space 6}Western  {c |}{col 16}{res}{space 2} .6154723{col 28}{space 2} .0979066{col 39}{space 1}    6.29{col 48}{space 3}0.000{col 56}{space 4}  .423579{col 69}{space 3} .8073657
{txt}North Eastern  {c |}{col 16}{res}{space 2} .3764262{col 28}{space 2} .3345481{col 39}{space 1}    1.13{col 48}{space 3}0.261{col 56}{space 4} -.279276{col 69}{space 3} 1.032128
{txt}{space 8}Coast  {c |}{col 16}{res}{space 2}-.6425245{col 28}{space 2} .1279193{col 39}{space 1}   -5.02{col 48}{space 3}0.000{col 56}{space 4}-.8932417{col 69}{space 3}-.3918074
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} .4737719{col 28}{space 2}  .841889{col 39}{space 1}    0.56{col 48}{space 3}0.574{col 56}{space 4}  -1.1763{col 69}{space 3} 2.123844
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est1{txt} stored)

{com}. logit BothHumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-549.81968}  
Iteration 1:{space 3}log pseudolikelihood = {res:-517.84969}  
Iteration 2:{space 3}log pseudolikelihood = {res: -516.9078}  
Iteration 3:{space 3}log pseudolikelihood = {res:-516.90533}  
Iteration 4:{space 3}log pseudolikelihood = {res:-516.90533}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       977
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(6)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-516.90533{txt}{col 49}Pseudo R2{col 67}= {res}    0.0599

{txt}{ralign 80:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}    BothHumanA{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}religion {c |}
{space 4}Christian  {c |}{col 16}{res}{space 2}-.3380988{col 28}{space 2} .6947628{col 39}{space 1}   -0.49{col 48}{space 3}0.627{col 56}{space 4}-1.699809{col 69}{space 3} 1.023611
{txt}{space 7}Muslim  {c |}{col 16}{res}{space 2}-.5671562{col 28}{space 2} .5968098{col 39}{space 1}   -0.95{col 48}{space 3}0.342{col 56}{space 4}-1.736882{col 69}{space 3} .6025695
{txt}{space 14} {c |}
{space 5}Education {c |}{col 16}{res}{space 2} .1978977{col 28}{space 2} .0685528{col 39}{space 1}    2.89{col 48}{space 3}0.004{col 56}{space 4} .0635367{col 69}{space 3} .3322586
{txt}{space 11}Age {c |}{col 16}{res}{space 2} .0090857{col 28}{space 2} .0044781{col 39}{space 1}    2.03{col 48}{space 3}0.042{col 56}{space 4} .0003087{col 69}{space 3} .0178627
{txt}{space 9}Urban {c |}{col 16}{res}{space 2} .1797257{col 28}{space 2} .2120687{col 39}{space 1}    0.85{col 48}{space 3}0.397{col 56}{space 4}-.2359213{col 69}{space 3} .5953727
{txt}{space 8}Hunger {c |}{col 16}{res}{space 2}-.0783505{col 28}{space 2} .0524244{col 39}{space 1}   -1.49{col 48}{space 3}0.135{col 56}{space 4}-.1811004{col 69}{space 3} .0243994
{txt}{space 10}Male {c |}{col 16}{res}{space 2}-.0138045{col 28}{space 2} .1359919{col 39}{space 1}   -0.10{col 48}{space 3}0.919{col 56}{space 4}-.2803437{col 69}{space 3} .2527347
{txt}{space 9}masai {c |}{col 16}{res}{space 2} .6381334{col 28}{space 2} .8682013{col 39}{space 1}    0.74{col 48}{space 3}0.462{col 56}{space 4} -1.06351{col 69}{space 3} 2.339777
{txt}{space 8}somali {c |}{col 16}{res}{space 2} .2758882{col 28}{space 2} 1.301273{col 39}{space 1}    0.21{col 48}{space 3}0.832{col 56}{space 4}-2.274561{col 69}{space 3} 2.826337
{txt}{space 2}pastoralists {c |}{col 16}{res}{space 2}-.2126358{col 28}{space 2} .3481144{col 39}{space 1}   -0.61{col 48}{space 3}0.541{col 56}{space 4}-.8949275{col 69}{space 3} .4696558
{txt}{space 14} {c |}
{space 8}region {c |}
{space 6}Central  {c |}{col 16}{res}{space 2}-.3139034{col 28}{space 2} .1412688{col 39}{space 1}   -2.22{col 48}{space 3}0.026{col 56}{space 4}-.5907852{col 69}{space 3}-.0370217
{txt}{space 6}Eastern  {c |}{col 16}{res}{space 2}-1.034685{col 28}{space 2} .1432983{col 39}{space 1}   -7.22{col 48}{space 3}0.000{col 56}{space 4}-1.315544{col 69}{space 3}-.7538253
{txt}{space 2}Rift Valley  {c |}{col 16}{res}{space 2}-.2038342{col 28}{space 2}  .130663{col 39}{space 1}   -1.56{col 48}{space 3}0.119{col 56}{space 4} -.459929{col 69}{space 3} .0522606
{txt}{space 7}Nyanza  {c |}{col 16}{res}{space 2}-.3945514{col 28}{space 2} .1262903{col 39}{space 1}   -3.12{col 48}{space 3}0.002{col 56}{space 4}-.6420758{col 69}{space 3} -.147027
{txt}{space 6}Western  {c |}{col 16}{res}{space 2} .5938323{col 28}{space 2} .1650438{col 39}{space 1}    3.60{col 48}{space 3}0.000{col 56}{space 4} .2703524{col 69}{space 3} .9173123
{txt}North Eastern  {c |}{col 16}{res}{space 2}-.1170777{col 28}{space 2} .9396947{col 39}{space 1}   -0.12{col 48}{space 3}0.901{col 56}{space 4}-1.958845{col 69}{space 3}  1.72469
{txt}{space 8}Coast  {c |}{col 16}{res}{space 2}-.5457788{col 28}{space 2} .1682328{col 39}{space 1}   -3.24{col 48}{space 3}0.001{col 56}{space 4}-.8755089{col 69}{space 3}-.2160486
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} .6385324{col 28}{space 2} .7594657{col 39}{space 1}    0.84{col 48}{space 3}0.400{col 56}{space 4} -.849993{col 69}{space 3} 2.127058
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est2{txt} stored)

{com}. esttab using AppTable_anthropo, tex replace b(3) se(3) label star(* .10 ** .05 *** .01) nogaps nomtitles nodepvars
{res}{txt}(note: file AppTable_anthropo.tex not found)
(output written to {browse  `"AppTable_anthropo.tex"'})

{com}. 
. 
. ******************************* Appendix Table 5: Religious Trust Model         
. global indvars masai somali pastoralists i.region Education Age Urban Hunger Male 
{txt}
{com}. global modelspecs [pweight= withinwt], cluster(region)
{txt}
{com}. eststo clear
{txt}
{com}. ologit CollectiveA_2 c.religioustrust##3.religion climateworsening BothHumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-721.32583}  
Iteration 1:{space 3}log pseudolikelihood = {res:-664.37747}  
Iteration 2:{space 3}log pseudolikelihood = {res:-663.29973}  
Iteration 3:{space 3}log pseudolikelihood = {res:-663.29696}  
Iteration 4:{space 3}log pseudolikelihood = {res:-663.29696}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       710
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-663.29696{txt}{col 49}Pseudo R2{col 67}= {res}    0.0804

{txt}{ralign 91:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}            CollectiveA_2{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      z{col 59}   P>|z|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}religioustrust {c |}{col 27}{res}{space 2}-.0702903{col 39}{space 2} .4137496{col 50}{space 1}   -0.17{col 59}{space 3}0.865{col 67}{space 4}-.8812246{col 80}{space 3}  .740644
{txt}{space 25} {c |}
{space 17}religion {c |}
{space 18}Muslim  {c |}{col 27}{res}{space 2} 2.130159{col 39}{space 2} .9825351{col 50}{space 1}    2.17{col 59}{space 3}0.030{col 67}{space 4} .2044258{col 80}{space 3} 4.055893
{txt}{space 25} {c |}
religion#c.religioustrust {c |}
{space 18}Muslim  {c |}{col 27}{res}{space 2}-3.708712{col 39}{space 2} 1.248225{col 50}{space 1}   -2.97{col 59}{space 3}0.003{col 67}{space 4}-6.155188{col 80}{space 3}-1.262236
{txt}{space 25} {c |}
{space 9}climateworsening {c |}{col 27}{res}{space 2}-.2004614{col 39}{space 2} .0857012{col 50}{space 1}   -2.34{col 59}{space 3}0.019{col 67}{space 4}-.3684327{col 80}{space 3}-.0324901
{txt}{space 15}BothHumanA {c |}{col 27}{res}{space 2}  1.13006{col 39}{space 2} .4136769{col 50}{space 1}    2.73{col 59}{space 3}0.006{col 67}{space 4} .3192685{col 80}{space 3} 1.940852
{txt}{space 20}masai {c |}{col 27}{res}{space 2}-.6185995{col 39}{space 2}  .329432{col 50}{space 1}   -1.88{col 59}{space 3}0.060{col 67}{space 4}-1.264274{col 80}{space 3} .0270753
{txt}{space 19}somali {c |}{col 27}{res}{space 2} .5074391{col 39}{space 2} .3611138{col 50}{space 1}    1.41{col 59}{space 3}0.160{col 67}{space 4} -.200331{col 80}{space 3} 1.215209
{txt}{space 13}pastoralists {c |}{col 27}{res}{space 2} -.649118{col 39}{space 2} .2571176{col 50}{space 1}   -2.52{col 59}{space 3}0.012{col 67}{space 4}-1.153059{col 80}{space 3}-.1451767
{txt}{space 25} {c |}
{space 19}region {c |}
{space 17}Central  {c |}{col 27}{res}{space 2} .7124753{col 39}{space 2} .1277583{col 50}{space 1}    5.58{col 59}{space 3}0.000{col 67}{space 4} .4620736{col 80}{space 3} .9628771
{txt}{space 17}Eastern  {c |}{col 27}{res}{space 2} .7332852{col 39}{space 2} .2007602{col 50}{space 1}    3.65{col 59}{space 3}0.000{col 67}{space 4} .3398025{col 80}{space 3} 1.126768
{txt}{space 13}Rift Valley  {c |}{col 27}{res}{space 2} .5447843{col 39}{space 2} .1226027{col 50}{space 1}    4.44{col 59}{space 3}0.000{col 67}{space 4} .3044875{col 80}{space 3} .7850812
{txt}{space 18}Nyanza  {c |}{col 27}{res}{space 2}   .77682{col 39}{space 2} .1212877{col 50}{space 1}    6.40{col 59}{space 3}0.000{col 67}{space 4} .5391005{col 80}{space 3}  1.01454
{txt}{space 17}Western  {c |}{col 27}{res}{space 2}-.4589368{col 39}{space 2} .1506617{col 50}{space 1}   -3.05{col 59}{space 3}0.002{col 67}{space 4}-.7542283{col 80}{space 3}-.1636452
{txt}{space 11}North Eastern  {c |}{col 27}{res}{space 2}  .202878{col 39}{space 2} .3502702{col 50}{space 1}    0.58{col 59}{space 3}0.562{col 67}{space 4} -.483639{col 80}{space 3}  .889395
{txt}{space 19}Coast  {c |}{col 27}{res}{space 2}-.0415686{col 39}{space 2} .2511281{col 50}{space 1}   -0.17{col 59}{space 3}0.869{col 67}{space 4}-.5337706{col 80}{space 3} .4506335
{txt}{space 25} {c |}
{space 16}Education {c |}{col 27}{res}{space 2} .0897386{col 39}{space 2} .0678678{col 50}{space 1}    1.32{col 59}{space 3}0.186{col 67}{space 4}-.0432799{col 80}{space 3}  .222757
{txt}{space 22}Age {c |}{col 27}{res}{space 2}  .001408{col 39}{space 2} .0051181{col 50}{space 1}    0.28{col 59}{space 3}0.783{col 67}{space 4}-.0086233{col 80}{space 3} .0114393
{txt}{space 20}Urban {c |}{col 27}{res}{space 2} .1074214{col 39}{space 2} .1415212{col 50}{space 1}    0.76{col 59}{space 3}0.448{col 67}{space 4} -.169955{col 80}{space 3} .3847978
{txt}{space 19}Hunger {c |}{col 27}{res}{space 2}-.1463159{col 39}{space 2} .0895558{col 50}{space 1}   -1.63{col 59}{space 3}0.102{col 67}{space 4} -.321842{col 80}{space 3} .0292103
{txt}{space 21}Male {c |}{col 27}{res}{space 2} .0849916{col 39}{space 2} .1741926{col 50}{space 1}    0.49{col 59}{space 3}0.626{col 67}{space 4}-.2564197{col 80}{space 3} .4264029
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}/cut1 {c |}{col 27}{res}{space 2}-.1632509{col 39}{space 2} .7737367{col 67}{space 4}-1.679747{col 80}{space 3} 1.353245
{txt}{space 20}/cut2 {c |}{col 27}{res}{space 2} 1.770556{col 39}{space 2} .7626655{col 67}{space 4} .2757595{col 80}{space 3} 3.265353
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est1{txt} stored)

{com}. ologit CollectiveA_2 c.religioustrust##3.religion c.statetrust##3.religion climateworsening BothHumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-721.32583}  
Iteration 1:{space 3}log pseudolikelihood = {res:-663.52822}  
Iteration 2:{space 3}log pseudolikelihood = {res:-662.46926}  
Iteration 3:{space 3}log pseudolikelihood = {res:-662.46629}  
Iteration 4:{space 3}log pseudolikelihood = {res:-662.46629}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       710
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-662.46629{txt}{col 49}Pseudo R2{col 67}= {res}    0.0816

{txt}{ralign 91:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}            CollectiveA_2{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      z{col 59}   P>|z|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}religioustrust {c |}{col 27}{res}{space 2}-.0604962{col 39}{space 2} .4195903{col 50}{space 1}   -0.14{col 59}{space 3}0.885{col 67}{space 4}-.8828782{col 80}{space 3} .7618857
{txt}{space 25} {c |}
{space 17}religion {c |}
{space 18}Muslim  {c |}{col 27}{res}{space 2} 1.063097{col 39}{space 2} .8303191{col 50}{space 1}    1.28{col 59}{space 3}0.200{col 67}{space 4}-.5642984{col 80}{space 3} 2.690493
{txt}{space 25} {c |}
religion#c.religioustrust {c |}
{space 18}Muslim  {c |}{col 27}{res}{space 2}-3.658567{col 39}{space 2}  1.15266{col 50}{space 1}   -3.17{col 59}{space 3}0.002{col 67}{space 4} -5.91774{col 80}{space 3}-1.399394
{txt}{space 25} {c |}
{space 15}statetrust {c |}{col 27}{res}{space 2}-.0821436{col 39}{space 2} .4866602{col 50}{space 1}   -0.17{col 59}{space 3}0.866{col 67}{space 4} -1.03598{col 80}{space 3} .8716928
{txt}{space 25} {c |}
{space 4}religion#c.statetrust {c |}
{space 18}Muslim  {c |}{col 27}{res}{space 2} 1.967824{col 39}{space 2} .5542785{col 50}{space 1}    3.55{col 59}{space 3}0.000{col 67}{space 4} .8814582{col 80}{space 3}  3.05419
{txt}{space 25} {c |}
{space 9}climateworsening {c |}{col 27}{res}{space 2}-.2021473{col 39}{space 2} .0719212{col 50}{space 1}   -2.81{col 59}{space 3}0.005{col 67}{space 4}-.3431101{col 80}{space 3}-.0611844
{txt}{space 15}BothHumanA {c |}{col 27}{res}{space 2} 1.151736{col 39}{space 2} .4266291{col 50}{space 1}    2.70{col 59}{space 3}0.007{col 67}{space 4} .3155581{col 80}{space 3} 1.987913
{txt}{space 20}masai {c |}{col 27}{res}{space 2}-.5998233{col 39}{space 2} .3373366{col 50}{space 1}   -1.78{col 59}{space 3}0.075{col 67}{space 4}-1.260991{col 80}{space 3} .0613443
{txt}{space 19}somali {c |}{col 27}{res}{space 2} .0149613{col 39}{space 2} .4095019{col 50}{space 1}    0.04{col 59}{space 3}0.971{col 67}{space 4}-.7876476{col 80}{space 3} .8175702
{txt}{space 13}pastoralists {c |}{col 27}{res}{space 2}-.6557734{col 39}{space 2} .2534464{col 50}{space 1}   -2.59{col 59}{space 3}0.010{col 67}{space 4}-1.152519{col 80}{space 3}-.1590275
{txt}{space 25} {c |}
{space 19}region {c |}
{space 17}Central  {c |}{col 27}{res}{space 2} .7285151{col 39}{space 2} .1268882{col 50}{space 1}    5.74{col 59}{space 3}0.000{col 67}{space 4} .4798189{col 80}{space 3} .9772114
{txt}{space 17}Eastern  {c |}{col 27}{res}{space 2} .7512228{col 39}{space 2} .2010272{col 50}{space 1}    3.74{col 59}{space 3}0.000{col 67}{space 4} .3572167{col 80}{space 3} 1.145229
{txt}{space 13}Rift Valley  {c |}{col 27}{res}{space 2} .5550721{col 39}{space 2}  .122564{col 50}{space 1}    4.53{col 59}{space 3}0.000{col 67}{space 4} .3148511{col 80}{space 3} .7952931
{txt}{space 18}Nyanza  {c |}{col 27}{res}{space 2} .7778258{col 39}{space 2} .1291675{col 50}{space 1}    6.02{col 59}{space 3}0.000{col 67}{space 4} .5246621{col 80}{space 3} 1.030989
{txt}{space 17}Western  {c |}{col 27}{res}{space 2}-.4542507{col 39}{space 2} .1498693{col 50}{space 1}   -3.03{col 59}{space 3}0.002{col 67}{space 4}-.7479891{col 80}{space 3}-.1605122
{txt}{space 11}North Eastern  {c |}{col 27}{res}{space 2} .5296823{col 39}{space 2} .3347705{col 50}{space 1}    1.58{col 59}{space 3}0.114{col 67}{space 4}-.1264558{col 80}{space 3}  1.18582
{txt}{space 19}Coast  {c |}{col 27}{res}{space 2}-.0413068{col 39}{space 2} .2548678{col 50}{space 1}   -0.16{col 59}{space 3}0.871{col 67}{space 4}-.5408384{col 80}{space 3} .4582248
{txt}{space 25} {c |}
{space 16}Education {c |}{col 27}{res}{space 2} .0881519{col 39}{space 2} .0661696{col 50}{space 1}    1.33{col 59}{space 3}0.183{col 67}{space 4}-.0415382{col 80}{space 3}  .217842
{txt}{space 22}Age {c |}{col 27}{res}{space 2} .0016821{col 39}{space 2} .0053478{col 50}{space 1}    0.31{col 59}{space 3}0.753{col 67}{space 4}-.0087994{col 80}{space 3} .0121635
{txt}{space 20}Urban {c |}{col 27}{res}{space 2} .1255855{col 39}{space 2}  .126572{col 50}{space 1}    0.99{col 59}{space 3}0.321{col 67}{space 4} -.122491{col 80}{space 3}  .373662
{txt}{space 19}Hunger {c |}{col 27}{res}{space 2}-.1427155{col 39}{space 2}  .097337{col 50}{space 1}   -1.47{col 59}{space 3}0.143{col 67}{space 4}-.3334924{col 80}{space 3} .0480615
{txt}{space 21}Male {c |}{col 27}{res}{space 2} .0817668{col 39}{space 2} .1753957{col 50}{space 1}    0.47{col 59}{space 3}0.641{col 67}{space 4}-.2620025{col 80}{space 3} .4255361
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}/cut1 {c |}{col 27}{res}{space 2}-.1634704{col 39}{space 2} .8312211{col 67}{space 4}-1.792634{col 80}{space 3} 1.465693
{txt}{space 20}/cut2 {c |}{col 27}{res}{space 2} 1.774685{col 39}{space 2} .8221063{col 67}{space 4} .1633865{col 80}{space 3} 3.385984
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est2{txt} stored)

{com}. ologit CollectiveA_2 religioustrust statetrust i.region if religiousID == 5 $modelspecs 

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-51.822078}  
Iteration 1:{space 3}log pseudolikelihood = {res:-41.390987}  
Iteration 2:{space 3}log pseudolikelihood = {res:-40.523202}  
Iteration 3:{space 3}log pseudolikelihood = {res:-40.418077}  
Iteration 4:{space 3}log pseudolikelihood = {res:-40.395534}  
Iteration 5:{space 3}log pseudolikelihood = {res:-40.390696}  
Iteration 6:{space 3}log pseudolikelihood = {res:-40.389718}  
Iteration 7:{space 3}log pseudolikelihood = {res:-40.389495}  
Iteration 8:{space 3}log pseudolikelihood = {res:-40.389439}  
Iteration 9:{space 3}log pseudolikelihood = {res:-40.389429}  
Iteration 10:{space 2}log pseudolikelihood = {res:-40.389427}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}        52
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(4)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-40.389427{txt}{col 49}Pseudo R2{col 67}= {res}    0.2206

{txt}{ralign 80:(Std. Err. adjusted for {res:6} clusters in region)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1} CollectiveA_2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
religioustrust {c |}{col 16}{res}{space 2}-3.612481{col 28}{space 2} 1.074768{col 39}{space 1}   -3.36{col 48}{space 3}0.001{col 56}{space 4}-5.718986{col 69}{space 3}-1.505975
{txt}{space 4}statetrust {c |}{col 16}{res}{space 2} 2.008412{col 28}{space 2}  .993443{col 39}{space 1}    2.02{col 48}{space 3}0.043{col 56}{space 4} .0612996{col 69}{space 3} 3.955524
{txt}{space 14} {c |}
{space 8}region {c |}
{space 6}Eastern  {c |}{col 16}{res}{space 2}-17.48206{col 28}{space 2} 1.127906{col 39}{space 1}  -15.50{col 48}{space 3}0.000{col 56}{space 4}-19.69272{col 69}{space 3}-15.27141
{txt}{space 2}Rift Valley  {c |}{col 16}{res}{space 2} 18.56841{col 28}{space 2} 1.135047{col 39}{space 1}   16.36{col 48}{space 3}0.000{col 56}{space 4} 16.34376{col 69}{space 3} 20.79306
{txt}{space 7}Nyanza  {c |}{col 16}{res}{space 2}-17.48206{col 28}{space 2} 1.127906{col 39}{space 1}  -15.50{col 48}{space 3}0.000{col 56}{space 4}-19.69272{col 69}{space 3}-15.27141
{txt}North Eastern  {c |}{col 16}{res}{space 2}-2.348453{col 28}{space 2} .1805954{col 39}{space 1}  -13.00{col 48}{space 3}0.000{col 56}{space 4}-2.702413{col 69}{space 3}-1.994493
{txt}{space 8}Coast  {c |}{col 16}{res}{space 2}-2.637458{col 28}{space 2} .3274221{col 39}{space 1}   -8.06{col 48}{space 3}0.000{col 56}{space 4}-3.279194{col 69}{space 3}-1.995723
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2} -4.48941{col 28}{space 2} 1.041036{col 56}{space 4}-6.529803{col 69}{space 3}-2.449017
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2}-1.682758{col 28}{space 2} .6713527{col 56}{space 4}-2.998585{col 69}{space 3}-.3669306
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 3 observations completely determined.{txt}  Standard errors questionable.{p_end}

{com}.         eststo
{txt}({res}est3{txt} stored)

{com}. ologit CollectiveA_2 religioustrust statetrust ethnicgrievance kenyan_v_ethnicID climateworsening BothHumanA $indvars if religiousID == 5 $modelspecs

{txt}note: masai omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-45.682505}  
Iteration 1:{space 3}log pseudolikelihood = {res:-26.106409}  
Iteration 2:{space 3}log pseudolikelihood = {res:-23.479582}  
Iteration 3:{space 3}log pseudolikelihood = {res:-22.037608}  
Iteration 4:{space 3}log pseudolikelihood = {res:-21.978866}  
Iteration 5:{space 3}log pseudolikelihood = {res:-21.972531}  
Iteration 6:{space 3}log pseudolikelihood = {res:-21.971227}  
Iteration 7:{space 3}log pseudolikelihood = {res:-21.970944}  
Iteration 8:{space 3}log pseudolikelihood = {res:-21.970881}  
Iteration 9:{space 3}log pseudolikelihood = {res:-21.970866}  
Iteration 10:{space 2}log pseudolikelihood = {res:-21.970862}  
Iteration 11:{space 2}log pseudolikelihood = {res:-21.970862}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}        49
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(4)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-21.970862{txt}{col 49}Pseudo R2{col 67}= {res}    0.5191

{txt}{ralign 83:(Std. Err. adjusted for {res:6} clusters in region)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}    CollectiveA_2{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}religioustrust {c |}{col 19}{res}{space 2}-8.634345{col 31}{space 2} 1.706643{col 42}{space 1}   -5.06{col 51}{space 3}0.000{col 59}{space 4} -11.9793{col 72}{space 3}-5.289387
{txt}{space 7}statetrust {c |}{col 19}{res}{space 2} 6.100055{col 31}{space 2} 1.320854{col 42}{space 1}    4.62{col 51}{space 3}0.000{col 59}{space 4}  3.51123{col 72}{space 3} 8.688881
{txt}{space 2}ethnicgrievance {c |}{col 19}{res}{space 2}  3.23961{col 31}{space 2} .2102153{col 42}{space 1}   15.41{col 51}{space 3}0.000{col 59}{space 4} 2.827596{col 72}{space 3} 3.651625
{txt}kenyan_v_ethnicID {c |}{col 19}{res}{space 2} 1.618964{col 31}{space 2} .6706884{col 42}{space 1}    2.41{col 51}{space 3}0.016{col 59}{space 4}  .304439{col 72}{space 3} 2.933489
{txt}{space 1}climateworsening {c |}{col 19}{res}{space 2}-2.397459{col 31}{space 2} .8280011{col 42}{space 1}   -2.90{col 51}{space 3}0.004{col 59}{space 4}-4.020312{col 72}{space 3}-.7746071
{txt}{space 7}BothHumanA {c |}{col 19}{res}{space 2} 5.911715{col 31}{space 2} 1.122582{col 42}{space 1}    5.27{col 51}{space 3}0.000{col 59}{space 4} 3.711495{col 72}{space 3} 8.111935
{txt}{space 12}masai {c |}{col 19}{res}{space 2}        0{col 31}{txt}  (omitted)
{space 11}somali {c |}{col 19}{res}{space 2}-5.806754{col 31}{space 2} .8105955{col 42}{space 1}   -7.16{col 51}{space 3}0.000{col 59}{space 4}-7.395492{col 72}{space 3}-4.218016
{txt}{space 5}pastoralists {c |}{col 19}{res}{space 2} .0795433{col 31}{space 2} .4073995{col 42}{space 1}    0.20{col 51}{space 3}0.845{col 59}{space 4}-.7189451{col 72}{space 3} .8780316
{txt}{space 17} {c |}
{space 11}region {c |}
{space 9}Eastern  {c |}{col 19}{res}{space 2}-15.01559{col 31}{space 2} 2.838504{col 42}{space 1}   -5.29{col 51}{space 3}0.000{col 59}{space 4}-20.57896{col 72}{space 3}-9.452223
{txt}{space 5}Rift Valley  {c |}{col 19}{res}{space 2} 17.80841{col 31}{space 2}  1.32501{col 42}{space 1}   13.44{col 51}{space 3}0.000{col 59}{space 4} 15.21144{col 72}{space 3} 20.40538
{txt}{space 10}Nyanza  {c |}{col 19}{res}{space 2} -21.7068{col 31}{space 2} 1.168713{col 42}{space 1}  -18.57{col 51}{space 3}0.000{col 59}{space 4}-23.99744{col 72}{space 3}-19.41616
{txt}{space 3}North Eastern  {c |}{col 19}{res}{space 2} 2.216452{col 31}{space 2} .5271835{col 42}{space 1}    4.20{col 51}{space 3}0.000{col 59}{space 4} 1.183191{col 72}{space 3} 3.249713
{txt}{space 11}Coast  {c |}{col 19}{res}{space 2}-2.494192{col 31}{space 2} .3600912{col 42}{space 1}   -6.93{col 51}{space 3}0.000{col 59}{space 4}-3.199958{col 72}{space 3}-1.788426
{txt}{space 17} {c |}
{space 8}Education {c |}{col 19}{res}{space 2}-.1012283{col 31}{space 2} .3621305{col 42}{space 1}   -0.28{col 51}{space 3}0.780{col 59}{space 4} -.810991{col 72}{space 3} .6085344
{txt}{space 14}Age {c |}{col 19}{res}{space 2} -.107111{col 31}{space 2} .0328596{col 42}{space 1}   -3.26{col 51}{space 3}0.001{col 59}{space 4}-.1715147{col 72}{space 3}-.0427074
{txt}{space 12}Urban {c |}{col 19}{res}{space 2}  .814964{col 31}{space 2} .8141892{col 42}{space 1}    1.00{col 51}{space 3}0.317{col 59}{space 4}-.7808175{col 72}{space 3} 2.410746
{txt}{space 11}Hunger {c |}{col 19}{res}{space 2}-1.088863{col 31}{space 2}  .240798{col 42}{space 1}   -4.52{col 51}{space 3}0.000{col 59}{space 4}-1.560818{col 72}{space 3}-.6169074
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .3034857{col 31}{space 2}  .519885{col 42}{space 1}    0.58{col 51}{space 3}0.559{col 59}{space 4}-.7154701{col 72}{space 3} 1.322442
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-7.493029{col 31}{space 2} 1.347017{col 59}{space 4}-10.13313{col 72}{space 3}-4.852925
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-2.831743{col 31}{space 2}  .854636{col 59}{space 4}-4.506799{col 72}{space 3}-1.156687
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 3 observations completely determined.{txt}  Standard errors questionable.{p_end}

{com}.         eststo
{txt}({res}est4{txt} stored)

{com}. esttab using AppTable_religtrust, tex replace b(3) se(3) label star(* .10 ** .05 *** .01) nogaps nomtitles nodepvars
{res}{txt}(note: file AppTable_religtrust.tex not found)
(output written to {browse  `"AppTable_religtrust.tex"'})

{com}. 
. 
. ******************************* Appendix Table 6: Alternative Measures to State Trust
. global indvars masai somali pastoralists i.region Education Age Urban Hunger Male 
{txt}
{com}. global modelspecs [pweight= withinwt], cluster(region)
{txt}
{com}. eststo clear
{txt}
{com}. ologit CollectiveA_2 c.infoaccess##Musl climateworsening BothHumanA $indvars $modelspecs

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-722.74616}  
Iteration 1:{space 3}log pseudolikelihood = {res:-668.31001}  
Iteration 2:{space 3}log pseudolikelihood = {res:-667.26679}  
Iteration 3:{space 3}log pseudolikelihood = {res:-667.26253}  
Iteration 4:{space 3}log pseudolikelihood = {res:-667.26253}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       714
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-667.26253{txt}{col 49}Pseudo R2{col 67}= {res}    0.0768

{txt}{ralign 83:(Std. Err. adjusted for {res:8} clusters in region)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}    CollectiveA_2{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}infoaccess {c |}{col 19}{res}{space 2} .4471097{col 31}{space 2} .2630264{col 42}{space 1}    1.70{col 51}{space 3}0.089{col 59}{space 4}-.0684125{col 72}{space 3}  .962632
{txt}{space 11}1.Musl {c |}{col 19}{res}{space 2} -1.84048{col 31}{space 2} .5078106{col 42}{space 1}   -3.62{col 51}{space 3}0.000{col 59}{space 4} -2.83577{col 72}{space 3} -.845189
{txt}{space 17} {c |}
Musl#c.infoaccess {c |}
{space 15}1  {c |}{col 19}{res}{space 2} 1.558944{col 31}{space 2} .8609832{col 42}{space 1}    1.81{col 51}{space 3}0.070{col 59}{space 4}-.1285515{col 72}{space 3}  3.24644
{txt}{space 17} {c |}
{space 1}climateworsening {c |}{col 19}{res}{space 2}-.1697097{col 31}{space 2} .0686037{col 42}{space 1}   -2.47{col 51}{space 3}0.013{col 59}{space 4}-.3041705{col 72}{space 3} -.035249
{txt}{space 7}BothHumanA {c |}{col 19}{res}{space 2} 1.152374{col 31}{space 2} .3942273{col 42}{space 1}    2.92{col 51}{space 3}0.003{col 59}{space 4} .3797026{col 72}{space 3} 1.925045
{txt}{space 12}masai {c |}{col 19}{res}{space 2}-.6134089{col 31}{space 2} .3422881{col 42}{space 1}   -1.79{col 51}{space 3}0.073{col 59}{space 4}-1.284281{col 72}{space 3} .0574634
{txt}{space 11}somali {c |}{col 19}{res}{space 2} .2384008{col 31}{space 2} .3728888{col 42}{space 1}    0.64{col 51}{space 3}0.523{col 59}{space 4}-.4924478{col 72}{space 3} .9692493
{txt}{space 5}pastoralists {c |}{col 19}{res}{space 2}-.6237118{col 31}{space 2} .2617801{col 42}{space 1}   -2.38{col 51}{space 3}0.017{col 59}{space 4}-1.136791{col 72}{space 3}-.1106323
{txt}{space 17} {c |}
{space 11}region {c |}
{space 9}Central  {c |}{col 19}{res}{space 2}  .674942{col 31}{space 2} .1080091{col 42}{space 1}    6.25{col 51}{space 3}0.000{col 59}{space 4}  .463248{col 72}{space 3}  .886636
{txt}{space 9}Eastern  {c |}{col 19}{res}{space 2} .6776985{col 31}{space 2} .1692805{col 42}{space 1}    4.00{col 51}{space 3}0.000{col 59}{space 4} .3459149{col 72}{space 3} 1.009482
{txt}{space 5}Rift Valley  {c |}{col 19}{res}{space 2} .4801633{col 31}{space 2} .1008069{col 42}{space 1}    4.76{col 51}{space 3}0.000{col 59}{space 4} .2825854{col 72}{space 3} .6777411
{txt}{space 10}Nyanza  {c |}{col 19}{res}{space 2} .6945951{col 31}{space 2} .1125313{col 42}{space 1}    6.17{col 51}{space 3}0.000{col 59}{space 4} .4740378{col 72}{space 3} .9151523
{txt}{space 9}Western  {c |}{col 19}{res}{space 2}-.4989481{col 31}{space 2} .1088121{col 42}{space 1}   -4.59{col 51}{space 3}0.000{col 59}{space 4}-.7122158{col 72}{space 3}-.2856804
{txt}{space 3}North Eastern  {c |}{col 19}{res}{space 2}-.0926218{col 31}{space 2} .2463005{col 42}{space 1}   -0.38{col 51}{space 3}0.707{col 59}{space 4}-.5753619{col 72}{space 3} .3901183
{txt}{space 11}Coast  {c |}{col 19}{res}{space 2} .1071644{col 31}{space 2}   .19478{col 42}{space 1}    0.55{col 51}{space 3}0.582{col 59}{space 4}-.2745974{col 72}{space 3} .4889262
{txt}{space 17} {c |}
{space 8}Education {c |}{col 19}{res}{space 2} .0737802{col 31}{space 2} .0653176{col 42}{space 1}    1.13{col 51}{space 3}0.259{col 59}{space 4}  -.05424{col 72}{space 3} .2018004
{txt}{space 14}Age {c |}{col 19}{res}{space 2} .0017685{col 31}{space 2} .0046979{col 42}{space 1}    0.38{col 51}{space 3}0.707{col 59}{space 4}-.0074392{col 72}{space 3} .0109762
{txt}{space 12}Urban {c |}{col 19}{res}{space 2} .1331853{col 31}{space 2} .1270708{col 42}{space 1}    1.05{col 51}{space 3}0.295{col 59}{space 4}-.1158688{col 72}{space 3} .3822394
{txt}{space 11}Hunger {c |}{col 19}{res}{space 2}-.1433076{col 31}{space 2} .0913541{col 42}{space 1}   -1.57{col 51}{space 3}0.117{col 59}{space 4}-.3223583{col 72}{space 3} .0357431
{txt}{space 13}Male {c |}{col 19}{res}{space 2} .0806938{col 31}{space 2}  .174489{col 42}{space 1}    0.46{col 51}{space 3}0.644{col 59}{space 4}-.2612984{col 72}{space 3}  .422686
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}  .039932{col 31}{space 2}    .9216{col 59}{space 4}-1.766371{col 72}{space 3} 1.846235
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2} 1.971452{col 31}{space 2} .8866133{col 59}{space 4} .2337218{col 72}{space 3} 3.709182
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         eststo
{txt}({res}est1{txt} stored)

{com}. esttab, b(3) se(3) label star(* .10 ** .05 *** .01) wide nopar
{res}
{txt}{hline 49}
{txt}                              (1)                
{txt}                     Q78: Ordin~t                
{txt}{hline 49}
{res}Q78: Ordinary Keny~t                             {txt}
{txt}infoaccess          {res}        0.447*          0.263{txt}
{txt}Q98 Muslim, Sunni ~l{res}        0.000               .{txt}
{txt}Q98 Muslim, Sunni ~l{res}       -1.840***        0.508{txt}
{txt}Q98 Muslim, Sunni ~l{res}        0.000               .{txt}
{txt}Q98 Muslim, Sunni ~l{res}        1.559*          0.861{txt}
{txt}Climate Worsening   {res}       -0.170**         0.069{txt}
{txt}Q75: dummy for any~m{res}        1.152***        0.394{txt}
{txt}Masai/Samburu       {res}       -0.613*          0.342{txt}
{txt}Somali              {res}        0.238           0.373{txt}
{txt}Pastoralist         {res}       -0.624**         0.262{txt}
{txt}Nairobi             {res}        0.000               .{txt}
{txt}Central             {res}        0.675***        0.108{txt}
{txt}Eastern             {res}        0.678***        0.169{txt}
{txt}Rift Valley         {res}        0.480***        0.101{txt}
{txt}Nyanza              {res}        0.695***        0.113{txt}
{txt}Western             {res}       -0.499***        0.109{txt}
{txt}North Eastern       {res}       -0.093           0.246{txt}
{txt}Coast               {res}        0.107           0.195{txt}
{txt}Education           {res}        0.074           0.065{txt}
{txt}Age                 {res}        0.002           0.005{txt}
{txt}Urban               {res}        0.133           0.127{txt}
{txt}Food shortage freq~y{res}       -0.143           0.091{txt}
{txt}Male                {res}        0.081           0.174{txt}
{hline 49}
{res}/                                                {txt}
{txt}cut1                {res}        0.040           0.922{txt}
{txt}cut2                {res}        1.971**         0.887{txt}
{txt}{hline 49}
{txt}Observations        {res}          714                {txt}
{txt}{hline 49}
{txt}Standard errors in second column
{txt}* p<.10, ** p<.05, *** p<.01

{com}. 
.         
. 
. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\aesmith2\Dropbox\Work\research in progress\Environmentalism and religion work\outputs\Kenya-Brazil\Writing and Presentations\Perspectives\Conditional Accept Submission\Honig Smith Bleck Replication Log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 8 Dec 2020, 19:21:27
{txt}{.-}
{smcl}
{txt}{sf}{ul off}